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Objective: To analyze changes in the muscular fat fraction (FF) during immobilization at the intensive care unit (ICU) using dual-energy CT (DECT) and evaluate the predictive value of the DECT FF as a new imaging biomarker for morbidity and survival. Methods: Immobilized ICU patients (n = 81, 43.2% female, 60.3 ± 12.7 years) were included, who received two dual-source DECT scans (CT1, CT2) within a minimum interval of 10 days between 11/2019 and 09/2022. The DECT FF was quantified for the posterior paraspinal muscle by two radiologists using material decomposition. The skeletal muscle index (SMI), muscle radiodensity attenuation (MRA), subcutaneous-/ visceral adipose tissue area (SAT, VAT), and waist circumference (WC) were assessed. Reasons for ICU admission, clinical scoring systems, therapeutic regimes, and in-hospital mortality were noted. Linear mixed models, Cox regression, and intraclass correlation coefficients were employed. Results: Between CT1 and CT2 (median 21 days), the DECT FF increased (from 20.9% ± 12.0 to 27.0% ± 12.0, p = 0.001). The SMI decreased (35.7 cm2/m2 ± 8.8 to 31.1 cm2/m2 ± 7.6, p < 0.001) as did the MRA (29 HU ± 10 to 26 HU ± 11, p = 0.009). WC, SAT, and VAT did not change. In-hospital mortality was 61.5%. In multivariable analyses, only the change in DECT FF was associated with in-hospital mortality (hazard ratio (HR) 9.20 [1.78–47.71], p = 0.008), renal replacement therapy (HR 48.67 [9.18–258.09], p < 0.001), and tracheotomy at ICU (HR 37.22 [5.66–245.02], p < 0.001). Inter-observer reproducibility of DECT FF measurements was excellent (CT1: 0.98 [0.97; 0.99], CT2: 0.99 [0.96–0.99]). Conclusion: The DECT FF appears to be suitable for detecting increasing myosteatosis. It seems to have predictive value as a new imaging biomarker for ICU patients. Clinical relevance statement: The dual-energy CT muscular fat fraction appears to be a robust imaging biomarker to detect and monitor myosteatosis. It has potential for prognosticating, risk stratifying, and thereby guiding therapeutic nutritional regimes and physiotherapy in critically ill patients. Key Points: The dual-energy CT muscular fat fraction detects increasing myosteatosis caused by immobilization. Change in dual-energy CT muscular fat fraction was a predictor of in-hospital morbidity and mortality. Dual-energy CT muscular fat fraction had a predictive value superior to established CT body composition parameters. [ABSTRACT FROM AUTHOR]

Objectives: To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model. Materials and methods: Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed. Results: A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich–Pyle (GP)/Tanner–Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91. Conclusion: The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model. Clinical relevance statement: This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods. Key Points: Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph. [ABSTRACT FROM AUTHOR]

Objective: To determine if macroscopic intralesional fat detected in bone lesions on CT by Hounsfield unit (HU) measurement and on MRI by macroscopic assessment excludes malignancy. Materials and methods: All consecutive CT-guided core needle biopsies (CNB) of non-spinal bone lesions performed at a tertiary center between December 2005 and September 2021 were reviewed. Demographic and histopathology data were recorded. All cases with malignant histopathology were selected, and imaging studies were reviewed. Two independent readers performed CT HU measurements on all bone lesions using a circular region of interest (ROI) to quantitate intralesional fat density (mean HU < −30). MRI images were reviewed to qualitatively assess for macroscopic intralesional fat signal in a subset of patients. Inter-reader agreement was assessed with Cronbach's alpha and intraclass correlation coefficient. Results: In 613 patients (mean age 62.9 years (range 19–95 years), 47.6% female), CT scans from the CNB of 613 malignant bone lesions were reviewed, and 212 cases had additional MRI images. Only 3 cases (0.5%) demonstrated macroscopic intralesional fat on either CT or MRI. One case demonstrated macroscopic intralesional fat density on CT in a case of metastatic prostate cancer. Two cases demonstrated macroscopic intralesional fat signal on MRI in cases of chondrosarcoma and osteosarcoma. Inter-reader agreement was excellent (Cronbach's alpha, 0.95–0.98; intraclass correlation coefficient, 0.90–0.97). Conclusion: Malignant lesions rarely contain macroscopic intralesional fat on CT or MRI. While CT is effective in detecting macroscopic intralesional fat in primarily lytic lesions, MRI may be better for the assessment of heterogenous and infiltrative lesions with mixed lytic and sclerotic components. Clinical relevance statement: Macroscopic intralesional fat is rarely seen in malignant bone tumors and its presence can help to guide the diagnostic workup of bone lesions. Key Points: • Presence of macroscopic intralesional fat in bone lesions has been widely theorized as a sign of benignity, but there is limited supporting evidence in the literature. • CT and MRI are effective in evaluating for macroscopic intralesional fat in malignant bone lesions with excellent inter-reader agreement. • Macroscopic intralesional fat is rarely seen in malignant bone lesions. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a deep learning–based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans. Methods: A total of 763 knee MRI slices from 95 patients were included in the study, and 3393 anatomical landmarks were annotated for measuring sulcus angle (SA), trochlear facet asymmetry (TFA), trochlear groove depth (TGD) and lateral trochlear inclination (LTI) to assess trochlear dysplasia, and Insall-Salvati index (ISI), modified Insall-Salvati index (MISI), Caton Deschamps index (CDI) and patellotrochlear index (PTI) to assess patellar height. A U-Net based network was implemented to predict the landmarks' locations. The successful detection rate (SDR) and the mean absolute error (MAE) evaluation metrics were used to evaluate the performance of the network. The intraclass correlation coefficient (ICC) was also used to evaluate the reliability of the proposed framework to measure the mentioned PFI indices. Results: The developed models achieved good accuracy in predicting the landmarks' locations, with a maximum value for the MAE of 1.38 ± 0.76 mm. The results show that LTI, TGD, ISI, CDI and PTI can be measured with excellent reliability (ICC > 0.9), and SA, TFA and MISI can be measured with good reliability (ICC > 0.75), with the proposed framework. Conclusions: This study proposes a reliable approach with promising applicability for automatic patellar height and trochlear dysplasia assessment, assisting the radiologists in their clinical practice. Clinical relevance statement: The objective knee landmarks detection on MRI images provided by artificial intelligence may improve the reproducibility and reliability of the imaging evaluation of trochlear anatomy and patellar height, assisting radiologists in their clinical practice in the patellofemoral instability assessment. Key Points: • Imaging evaluation of patellofemoral instability is subjective and vulnerable to substantial intra and interobserver variability. • Patellar height and trochlear dysplasia are reliably assessed in MRI by means of artificial intelligence (AI). • The developed AI framework provides an objective evaluation of patellar height and trochlear dysplasia enhancing the clinical practice of the radiologists. [ABSTRACT FROM AUTHOR]

Objective: The prognostic stratification for oral tongue squamous cell carcinoma (OTSCC) is heavily based on postoperative pathological depth of invasion (pDOI). This study aims to propose a preoperative MR T-staging system based on tumor size for non-pT4 OTSCC. Methods: Retrospectively, 280 patients with biopsy-confirmed, non-metastatic, pT1-3 OTSCC, treated between January 2010 and December 2017, were evaluated. Multiple MR sequences, including axial T2-weighted imaging (WI), unenhanced T1WI, and axial, fat-suppressed coronal, and sagittal contrast-enhanced (CE) T1WI, were utilized to measure radiological depth of invasion (rDOI), tumor thickness, and largest diameter. Intra-class correlation (ICC) and univariate and multivariate analyses were used to evaluate measurement reproducibility, and factors' significance, respectively. Cutoff values were established using an exhaustive method. Results: Intra-observer (ICC = 0.81–0.94) and inter-observer (ICC = 0.79–0.90) reliability were excellent for rDOI measurements, and all measurements were significantly associated with overall survival (OS) (all p <.001). Measuring the rDOI on axial CE-T1WI with cutoffs of 8 mm and 12 mm yielded an optimal MR T-staging system for rT1-3 disease (5-year OS of rT1 vs rT2 vs rT3: 94.0% vs 72.8% vs 57.5%). Using multivariate analyses, the proposed T-staging exhibited increasingly worse OS (hazard ratio of rT2 and rT3 versus rT1, 3.56 [1.35–9.6], p =.011; 4.33 [1.59–11.74], p =.004; respectively), which outperformed pathological T-staging based on nonoverlapping Kaplan–Meier curves and improved C-index (0.682 vs. 0.639, p <.001). Conclusions: rDOI is a critical predictor of OTSCC mortality and facilitates preoperative prognostic stratification, which should be considered in future oral subsite MR T-staging. Clinical relevance statement: Utilizing axial CE-T1WI, an MR T-staging system for non-pT4 OTSCC was developed by employing rDOI measurement with optimal thresholds of 8 mm and 12 mm, which is comparable with pathological staging and merits consideration in future preoperative oral subsite planning. Key Points: • Tumor morphology, measuring sequences, and observers could impact MR-derived measurements and compromise the consistency with histology. • MR-derived measurements, including radiological depth of invasion (rDOI), tumor thickness, and largest diameter, have a prognostic impact on OS (all p <.001). • rDOI with cutoffs of 8 mm and 12 mm on axial CE-T1WI is an optimal predictor of OS and could facilitate risk stratification in non-pT4 OTSCC disease. [ABSTRACT FROM AUTHOR]

Objectives: Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients. Materials and methods: Vertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC). Results: The ICCs between the readers ranged from 0.90 (95% CI 0.83–0.94) to 0.93 (95% CI 0.88–0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71–0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87–0.96) to 0.97 (95% CI 0.94–0.98). Conclusion: The Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured. Clinical relevance statement: Our AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care. Key Points: • While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis. • The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis. • Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate deep learning–based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. Methods: This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland–Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. Results: All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91–0.93). Conclusion: The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. Clinical relevance statement: Deep learning–based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. Key Points: • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models. [ABSTRACT FROM AUTHOR]

Objectives: This study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation. Methods: A pooled NC of 3945 subjects (NCpool) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer's disease (AD) patients (55–82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NCpool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC. Results: The maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran's Q, p = 0.19). Conclusion: At least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study's context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used. Clinical relevance statement: The influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation. Key Points: • Software-based brain atrophy estimation often relies on normal cohorts for comparisons. • At least 15 subjects must be included in an age- and sex-specific normal cohort. • Using different normal cohorts does not influence regional atrophy estimation. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans. Methods: Abdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC). Results: Twenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396–0.814) to 0.785 (95%CI 0.676–0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034–0.500) to 0.562 (95%CI 0.337–0.729). Intraobserver agreement increased for four out of six observers. Conclusion: The PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement. Clinical relevance statement: This study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms. Key Points: • Subjective assessment of diagnostic image quality via Likert scale has limited reliability. • A pairwise comparison method improves the inter- and intraobserver agreement. • The pairwise comparison method is more reliable for CT optimization studies. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to evaluate a three-material decomposition algorithm for hepatic fat quantification using a dual-layer computed tomography (DL-CT) and MRI as reference standard on a large patient cohort. Method: A total of 104 patients were retrospectively included in our study, i.e., each patient had an MRI exam and a DL-CT exam in our institution within a maximum of 31 days. Four regions of interest (ROIs) were positioned blindly and similarly in the liver, by two independent readers on DL-CT and MRI images. For DL-CT exams, all imaging phases were included. Fat fraction agreement between CT and MRI was performed using intraclass correlation coefficients (ICC), determination coefficients R2, and Bland–Altman plots. Diagnostic performance was determined using sensitivity, specificity, and positive and negative predictive values. The cutoff for steatosis was 5%. Results: Correlation between MRI and CT data was excellent for all perfusion phases with ICC calculated at 0.99 for each phase. Determination coefficients R2 were also good for all perfusion phases (about 0.95 for all phases). Performance of DL-CT in the diagnosis of hepatic steatosis was good with sensitivity between 89 and 91% and specificity ranging from 75 to 80%, depending on the perfusion phase. The positive predictive value was ranging from 78 to 93% and the negative predictive value from 82 to 86%. Conclusion: Multi-material decomposition in DL-CT allows quantification of hepatic fat fraction with a good correlation to MRI data. Clinical relevance statement: The use of DL-CT allows for detection of hepatic steatosis. This is especially interesting as an opportunistic finding CT performed for other reasons, as early detection can help prevent or slowdown the development of liver metabolic disease. Key Points: • Hepatic fat fractions provided by the dual-layer CT algorithm is strongly correlated with that measured on MRI. • Dual-layer CT is accurate to detect hepatic steatosis ≥ 5%. • Dual-layer CT allows opportunistic detection of steatosis, on CT scan performed for various indications. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether using the diffusion levels (DLs) proposed by the European Society of Breast Imaging (EUSOBI) improves the diagnostic accuracy of breast MRI. Materials and methods: This retrospective study included 145 women who, between September 2019 and June 2020, underwent breast 1.5-T MRI with DWI. Reader 1 and reader 2 (R1-R2) independently assessed breast lesions using the BI-RADS on dynamic contrast-enhanced imaging and T2-weighted imaging. DWI was subsequently disclosed, allowing readers able to measure lesions ADC and subjectively express the overall risk of malignancy on a 1–5 Likert scale. ADCs were interpreted as a range of values corresponding to the EUSOBI DLs. The analysis evaluated the inter-reader agreement in measuring ADC and DLs, the per-DL malignancy rate, and accuracy for malignancy using ROC analysis against histological examination or a 3-year follow-up. Results: Lesions were malignant and showed non-mass enhancement in 67.7% and 76.1% of cases, respectively. ADC was measurable in 63.2%/66.7% of lesions (R1/R2), with a minimal discrepancy on Bland–Altman analysis and 0.948 (95%CI 0.925–0.965)/0.989 (95%CI 0.988–0.991) intraclass correlation coefficient in measuring ADC/DLs. The malignancy rate (R1/R2) increased from 0.5/0.5% ("very high" DL) to 96.0/96.8% ("very low" DL), as expected. Likert categorization showed larger areas under the curve than the BI-RADS for both R1 (0.91 versus 0.87; p = 0.0208) and R2 (0.91 versus 0.89; p = 0.1171), with improved specificity (81.5% versus 78.5% for R1 and 84.4% versus 81.2% for R2). Conclusion: Though ADC was not measurable in about one-third of lesions, DLs were categorized with excellent inter-reader agreement, improving the specificity for malignancy. Clinical relevance statement: DLs proposed by the EUSOBI are a reproducible tool to interpret the ADC of breast lesions and, in turn, to improve the specificity of breast MRI and reduce unnecessary breast biopsies. Key Points: • The European Society of Breast Imaging proposed diffusion levels for the interpretation of the apparent diffusion coefficient in diffusion-weighted imaging of the breast. • Adding diffusion levels to the interpretation of magnetic resonance imaging improved the diagnostic accuracy for breast cancer, especially in terms of specificity. • Diffusion levels can favor a more widespread and standardized use of diffusion-weighted imaging of the breast. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the application of black-blood CT (BBCT) in carotid artery wall imaging and its accuracy in disclosing stenosis rate and plaque burden of carotid artery. Methods: A total of 110 patients underwent contrast-enhanced CT scan with two phases, and BBCT images were obtained using contrast-enhancement (CE)-boost technology. Two radiologists independently scored subjective image quality on black-blood computerized tomography (BBCT) images using a 4-point scale and then further analyzed plaque types. The artery stenosis rate on BBCT was measured and compared with CTA. The plaque burden on BBCT was compared with that on high-resolution intracranial vessel wall MR imaging (VW-MR imaging). The kappa value and intraclass correlation coefficient (ICC) were used for consistency analysis. The diagnostic accuracy of BBCT for stenosis rate and plaque burden greater than 50% was evaluated by AUC. Results: The subjective image quality scores of BBCT had good consistency between the two readers (ICC = 0.836, p < 0.001). BBCT and CTA had a good consistency in the identification of stenosis rate (p < 0.001). There was good consistency between BBCT and VW-MR in diagnosis of plaque burden (p < 0.001). As for plaque burden over 50%, BBCT had good sensitivity (93.10%) and specificity (73.33%), with an AUC of 0.950 (95%CI 0.838–0.993). Compared with CTA, BBCT had higher consistency with VW-MR in disclosing low-density plaques and mixed plaques (ICC = 0.931 vs 0.858, p < 0.001). Conclusions: BBCT can not only display the carotid artery wall clearly but also accurately diagnose the stenosis rate and plaque burden of carotid artery. Clinical relevance statement: Black-blood CT, as a novel imaging technology, can assist clinicians and radiologists in better visualizing the structure of the vessel wall and plaques, especially for patients with contraindication to MRI. Key Points: • Black-blood CT can clearly visualize the carotid artery wall and plaque burden. • Black-blood CT is superior to conventional CTA with more accurate diagnosis of the carotid stenosis rate and plaque burden features. [ABSTRACT FROM AUTHOR]

Objectives: The Prostate Imaging for Recurrence Reporting (PI-RR) system has been recently proposed to promote standardisation in the MR assessment of prostate cancer (PCa) local recurrence after radical prostatectomy (RP) and radiation therapy (RT). This study aims to evaluate PI-RR's diagnostic accuracy, assess the inter-observer reliability among readers with variable experience, and correlate imaging results with anatomopathological and laboratory parameters. Methods: Patients who underwent a pelvic MRI for suspicion of PCa local recurrence after RP or RT were retrospectively enrolled (October 2017–February 2020). PI-RR scores were independently assessed for each patient by five readers with variable experience in prostate MRI (two senior and three junior radiologists). Biochemical data and histopathological features were collected. The reference standard was determined through biochemical, imaging, or histopathological follow-up data. Reader's diagnostic performance was assessed using contingency tables. Cohen's kappa coefficient (κ) and intraclass correlation coefficient (ICC) were calculated to measure inter-observer reliability. Results: The final cohort included 120 patients (median age, 72 years [IQR, 62–82]). Recurrence was confirmed in 106 (88.3%) patients. Considering a PI-RR score ≥ 3 as positive for recurrence, minimum and maximum diagnostic values among the readers were as follows: sensitivity 79–86%; specificity 64–86%; positive predictive value 95–98%; negative predictive value 33–46%; accuracy 79–87%. Regardless of reader's level of experience, the inter-observer reliability resulted good or excellent (κ ranges across all readers: 0.52–0.77), and ICC was 0.8. Prostate-specific antigen (PSA) velocity, baseline-PSA, and trigger-PSA resulted predictive of local recurrence at imaging. Conclusions: The PI-RR system is an effective tool for MRI evaluation of PCa local recurrence and facilitates uniformity among radiologists. Clinical relevance statement: This study confirmed the PI-RR system's good diagnostic accuracy for the MRI evaluation of PCa local recurrences. It showed high reproducibility among readers with variable experience levels, validating it as a promising standardisation tool for assessing patients with biochemical recurrence. Key Points: • In this retrospective study, the PI-RR system revealed promising diagnostic performances among five readers with different experience (sensitivity 79–86%; specificity 64–86%; accuracy 79–87%). • The inter-observer reliability among the five readers resulted good or excellent (κ ranges: 0.52–0.77) with an intraclass correlation coefficient of 0.8. • The PI-RR assessment score may facilitate standardisation and generalizability in the evaluation of prostate cancer local recurrence among radiologists. [ABSTRACT FROM AUTHOR]

Objectives: Evaluate the performance of a deep learning (DL)–based model for multiple sclerosis (MS) lesion segmentation and compare it to other DL and non-DL algorithms. Methods: This ambispective, multicenter study assessed the performance of a DL-based model for MS lesion segmentation and compared it to alternative DL- and non-DL-based methods. Models were tested on internal (n = 20) and external (n = 18) datasets from Latin America, and on an external dataset from Europe (n = 49). We also examined robustness by rescanning six patients (n = 6) from our MS clinical cohort. Moreover, we studied inter-human annotator agreement and discussed our findings in light of these results. Performance and robustness were assessed using intraclass correlation coefficient (ICC), Dice coefficient (DC), and coefficient of variation (CV). Results: Inter-human ICC ranged from 0.89 to 0.95, while spatial agreement among annotators showed a median DC of 0.63. Using expert manual segmentations as ground truth, our DL model achieved a median DC of 0.73 on the internal, 0.66 on the external, and 0.70 on the challenge datasets. The performance of our DL model exceeded that of the alternative algorithms on all datasets. In the robustness experiment, our DL model also achieved higher DC (ranging from 0.82 to 0.90) and lower CV (ranging from 0.7 to 7.9%) when compared to the alternative methods. Conclusion: Our DL-based model outperformed alternative methods for brain MS lesion segmentation. The model also proved to generalize well on unseen data and has a robust performance and low processing times both on real-world and challenge-based data. Clinical relevance statement: Our DL-based model demonstrated superior performance in accurately segmenting brain MS lesions compared to alternative methods, indicating its potential for clinical application with improved accuracy, robustness, and efficiency. Key Points: • Automated lesion load quantification in MS patients is valuable; however, more accurate methods are still necessary. • A novel deep learning model outperformed alternative MS lesion segmentation methods on multisite datasets. • Deep learning models are particularly suitable for MS lesion segmentation in clinical scenarios. [ABSTRACT FROM AUTHOR]

Objectives: The Alberta Stroke Program Early CT Score (ASPECTS) is a semi-quantitative method to evaluate the severity of early ischemic change on non-contrast computed tomography (NCCT) in patients with acute ischemic stroke (AIS). In this work, we propose an automated ASPECTS method based on large cohort of data and machine learning. Methods: For this study, we collected 3626 NCCT cases from multiple centers and annotated directly on this dataset by neurologists. Based on image analysis and machine learning methods, we constructed a two-stage machine learning model. The validity and reliability of this automated ASPECTS method were tested on an independent external validation set of 300 cases. Statistical analyses on the total ASPECTS, dichotomized ASPECTS, and region-level ASPECTS were presented. Results: On an independent external validation set of 300 cases, for the total ASPECTS results, the intraclass correlation coefficient between automated ASPECTS and expert-rated was 0.842. The agreement between ASPECTS threshold of ≥ 6 versus < 6 using a dichotomized method was moderate (κ = 0.438, 0.391–0.477), and the detection rate (sensitivity) was 86.5% for patients with ASPECTS threshold of ≥ 6. Compared with the results of previous studies, our method achieved a slight lead in sensitivity (67.8%) and AUC (0.845), with comparable accuracy (78.9%) and specificity (81.2%). Conclusion: The proposed automated ASPECTS method driven by a large cohort of NCCT images performed equally well compared with expert-rated ASPECTS. This work further demonstrates the validity and reliability of automated ASPECTS evaluation method. Clinical relevance statement: The automated ASPECTS method proposed by this study may help AIS patients to receive rapid intervention, but should not be used as a stand-alone diagnostic basis. Key Points: NCCT-based manual ASPECTS scores were poorly consistent. Machine learning can automate the ASPECTS scoring process. Machine learning model design based on large cohort data can effectively improve the consistency of ASPECTS scores. [ABSTRACT FROM AUTHOR]

Objectives: To explore the use of deep learning–constrained compressed sensing (DLCS) in improving image quality and acquisition time for 3D MRI of the brachial plexus. Methods: Fifty-four participants who underwent contrast-enhanced imaging and forty-one participants who underwent unenhanced imaging were included. Sensitivity encoding with an acceleration of 2 × 2 (SENSE4x), CS with an acceleration of 4 (CS4x), and DLCS with acceleration of 4 (DLCS4x) and 8 (DLCS8x) were used for MRI of the brachial plexus. Apparent signal-to-noise ratios (aSNRs), apparent contrast-to-noise ratios (aCNRs), and qualitative scores on a 4-point scale were evaluated and compared by ANOVA and the Friedman test. Interobserver agreement was evaluated by calculating the intraclass correlation coefficients. Results: DLCS4x achieved higher aSNR and aCNR than SENSE4x, CS4x, and DLCS8x (all p < 0.05). For the root segment of the brachial plexus, no statistically significant differences in the qualitative scores were found among the four sequences. For the trunk segment, DLCS4x had higher scores than SENSE4x (p = 0.04) in the contrast-enhanced group and had higher scores than SENSE4x and DLCS8x in the unenhanced group (all p < 0.05). For the divisions, cords, and branches, DLCS4x had higher scores than SENSE4x, CS4x, and DLCS8x (all p ≤ 0.01). No overt difference was found among SENSE4x, CS4x, and DLCS8x in any segment of the brachial plexus (all p > 0.05). Conclusions: In three-dimensional MRI for the brachial plexus, DLCS4x can improve image quality compared with SENSE4x and CS4x, and DLCS8x can maintain the image quality compared to SENSE4x and CS4x. Clinical relevance statement: Deep learning–constrained compressed sensing can improve the image quality or accelerate acquisition of 3D MRI of the brachial plexus, which should be benefit in evaluating the brachial plexus and its branches in clinical practice. Key Points: •Deep learning–constrained compressed sensing showed higher aSNR, aCNR, and qualitative scores for the brachial plexus than SENSE and CS at the same acceleration factor with similar scanning time. •Deep learning–constrained compressed sensing at acceleration factor of 8 had comparable aSNR, aCNR, and qualitative scores to SENSE4x and CS4x with approximately half the examination time. •Deep learning–constrained compressed sensing may be helpful in clinical practice for improving image quality and acquisition time in three-dimensional MRI of the brachial plexus. [ABSTRACT FROM AUTHOR]

Objectives: As structured reporting is increasingly used in the evaluation of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) for prostate cancer, there is a need to assess the reliability of these frameworks. This study aimed to evaluate the intra- and interreader agreement among readers with varying levels of experience using PSMA-RADS 1.0 for interpreting PSMA-PET/CT scans, even when blinded to clinical data, and therefore to determine the feasibility of implementing this reporting system in clinical practice. Methods: PSMA-PET/CT scans of 103 patients were independently evaluated by 4 readers with different levels of experience according to the reporting and data system (RADS) for PSMA-PET/CT imaging PSMA-RADS 1.0 at 2 time points within 6 weeks. For each scan, a maximum of five target lesions were freely chosen and stratified according to PSMA-RADS 1.0. Overall scan score and compartment-based scores were assessed. Intra- and interreader agreement was determined using the intraclass correlation coefficient (ICC). Results: PSMA-RADS 1.0 demonstrated excellent interreader agreement for both overall scan scores (ICC ≥ 0.91) and compartment-based scores (ICC ≥ 0.93) across all four readers. The framework showed excellent intrareader agreement for overall scan scores (ICC ≥ 0.86) and compartment-based scores (ICC ≥ 0.95), even among readers with varying levels of experience. Conclusions: PSMA-RADS 1.0 is a reliable method for assessing PSMA-PET/CT with strong consistency and agreement among readers. It shows great potential for establishing a standard approach to diagnosing and planning treatment for prostate cancer patients, and can be used confidently even by readers with less experience. Clinical relevance statement: This study underlines that PSMA-RADS 1.0 is a valuable and highly reliable scoring system for PSMA-PET/CT scans of prostate cancer patients and can be used confidently by radiologists with different levels of experience in routine clinical practice. Key Points: PSMA-RADS version 1.0 is a scoring system for PSMA-PET/CT scans. Its reproducibility needs to be analyzed in order to make it applicable to clinical practice. Excellent interreader and intrareader agreement for overall scan scores and compartment-based scores using PSMA-RADS 1.0 were seen in readers with varying levels of experience. PSMA-RADS 1.0 is a reliable tool for accurately diagnosing and planning treatment for prostate cancer patients, and can be used confidently in clinical routine. [ABSTRACT FROM AUTHOR]

Objectives: To describe the specific MRI characteristics of different pathologic subtypes of esophageal carcinoma (EC) Methods: This prospective study included EC patients who underwent esophageal MRI and esophagectomy between April 2015 and October 2021. Pathomorphological characteristics of EC such as localized type (LT), ulcerative type (UT), protruding type (PT), and infiltrative type (IT) were assessed by two radiologists relying on the imaging characteristics of tumor, especially the specific imaging findings on the continuity of the mucosa overlying the tumor, the opposing mucosa, mucosa linear thickening, and transmural growth pattern. Intraclass correlation coefficients (ICC) were calculated for the consistency between two readers. The associations of imaging characteristics with different pathologic subtypes were assessed using multilogistic regression model (MLR). Results: A total of 201 patients were identified on histopathology with a high inter-reader agreement (ICC = 0.991). LT showed intact mucosa overlying the tumor. IT showed transmural growth pattern extending from the mucosa to the adventitia and a "sandwich" appearance. The remaining normal mucosa on the opposing side was linear and nodular in UT. PT showed correlation with T1 staging and grade 1; IT showed correlation with T3 staging and grades 2–3. Four MLR models showed high predictive performance on the test set with AUCs of 0.94 (LT), 0.87 (PT), 0.96 (IT), and 0.97 (UT), respectively, and the predictors that contributed most to the models matched the four specific characteristics. Conclusions: Different pathologic subtypes of EC displayed specific MR imaging characteristics, which could help predict T staging and the degree of pathological differentiation. Clinical relevance statement: Different pathologic subtypes of esophageal carcinoma displayed specific MR imaging characteristics, which correspond to differences in the degree of differentiation, T staging, and sensitivity to radiotherapy, and could also be one of the predictive factors of cause-specific survival and local progression-free rates. Key Points: Different types of EC had different characteristics on MR images. A total of 91/95 (96%) LTEC showed intact mucosa over the tumor, while masses or nodules are specific to PTEC; 21/27 (78%) ITEC showed a "sandwich" sign; and 33/35 (60%) UTEC showed linear and nodular opposing mucosa. In the association of tumor type with degree of differentiation and T staging, PTEC was predominantly associated with T1 and grade 1, and ITEC was associated with T3 and grades 2–3, while LTEC and UECT were likewise primarily linked with T2–3 and grades 2–3. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate and compare the diagnostic performance of CT-like images based on a 3D T1-weighted spoiled gradient-echo sequence (T1 GRE), an ultra-short echo time sequence (UTE), and a 3D T1-weighted spoiled multi-echo gradient-echo sequence (FRACTURE) with conventional CT in patients with suspected osseous shoulder pathologies. Materials and methods: Patients with suspected traumatic dislocation of the shoulder (n = 46, mean age 40 ± 14.5 years, 19 women) were prospectively recruited and received 3-T MR imaging including 3D T1 GRE, UTE, and 3D FRACTURE sequences. CT was performed in patients with acute fractures and served as standard of reference (n = 25). Agreement of morphological features between the modalities was analyzed including the glenoid bone loss, Hill-Sachs interval, glenoid track, and the anterior straight-line length. Agreement between the modalities was assessed using Bland-Altman plots, Student's t-test, and Pearson's correlation coefficient. Inter- and intrareader assessment was evaluated with weighted Cohen's κ and intraclass correlation coefficient. Results: All osseous pathologies were detected accurately on all three CT-like sequences (n = 25, κ = 1.00). No significant difference in the percentage of glenoid bone loss was found between CT (mean ± standard deviation, 20.3% ± 8.0) and CT-like MR images (FRACTURE 20.6% ± 7.9, T1 GRE 20.4% ± 7.6, UTE 20.3% ± 7.7, p > 0.05). When comparing the different measurements on CT-like images, measurements performed using the UTE images correlated best with CT. Conclusion: Assessment of bony Bankart lesions and other osseous pathologies was feasible and accurate using CT-like images based on 3-T MRI compared with conventional CT. Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT. Clinical relevance statement: In an acute trauma setting, CT-like images based on a T1 GRE, UTE, or FRACTURE sequence might be a useful alternative to conventional CT scan sparing associated costs as well as radiation exposure. Key Points: • No significant differences were found for the assessment of the glenoid bone loss when comparing measurements of CT-like MR images with measurements of conventional CT images. • Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT whereas the FRACTURE sequence appeared to be the most robust regarding motion artifacts. • The T1 GRE sequence had the highest resolution with high bone contrast and detailed delineation of even small fractures but was more susceptible to motion artifacts. [ABSTRACT FROM AUTHOR]

Objectives: Epicardial adipose tissue (EAT) is a proposed marker of cardiovascular risk; however, clinical application may be limited by variability in post-processing software platforms. We assessed inter-vendor agreement of EAT volume (EATv) and attenuation on both contrast-enhanced (CE) and non-contrast CT (NCT) using a standard coronary CT reporting software (Vitrea), an EAT research-specific software (QFAT) and a freeware imaging software (OsiriX). Methods: Seventy-six consecutive patients undergoing simultaneous CE and NCT had complete volumetric EAT measurement. Between-software, within-software NCT vs. CE, and inter- and intra-observer agreement were evaluated with analysis by ANOVA (with post hoc adjustment), Bland-Altman with 95% levels of agreement (LoA) and intraclass correlation coefficient (ICC). Results: Mean EATv (freeware 53 ± 31 mL vs. research 93 ± 43 mL vs. coronary 157 ± 64 mL) and attenuation (freeware − 72 ± 25 HU vs. research − 75 ± 3 HU vs. coronary − 61 ± 10 HU) were significantly different between all vendors (ANOVA p < 0.001). EATv was consistently higher in NCT vs. CE for all software packages, with most reproducibility found in research software (bias 26 mL, 95% LoA: 2 to 56 mL), compared to freeware (bias 11 mL 95% LoA: − 46 mL to 69 mL) and coronary software (bias 10 mL 95% LoA: − 127 to 147 mL). Research software had more comparable NCT vs. CE attenuation (− 75 vs. − 72 HU) compared to freeware (− 72 vs. − 57 HU) and coronary (− 61 vs. − 39 HU). Excellent inter-observer agreement was seen with research (ICC 0.98) compared to freeware (ICC 0.73) and coronary software (ICC 0.75) with narrow LoA on Bland-Altman analysis. Conclusion: There are significant inter-vendor differences in EAT assessment. Our study suggests that research-specific software has better agreement and reproducibility compared to freeware or coronary software platforms. Key Points: • There are significant differences between EAT volume and attenuation values between software platforms, regardless of scan type. • Non-contrast scans routinely have higher mean EAT volume and attenuation; however, this finding is only consistently seen with research-specific software. • Of the three analyzed packages, research-specific software demonstrates the highest reproducibility, agreement, and reliability for both inter-scan and inter-observer agreement. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the image quality of an ultra-low contrast medium and radiation dose CT pulmonary angiography (CTPA) protocol for the diagnosis of acute pulmonary embolism using a clinical photon-counting detector (PCD) CT system and compare its performance to a dual-energy-(DE)-CTPA protocol on a conventional energy-integrating detector (EID) CT system. Methods: Sixty-four patients either underwent CTPA with the novel scan protocol on the PCD-CT scanner (32 patients, 25 mL, CTDIvol 2.5 mGy·cm) or conventional DE-CTPA on a third-generation dual-source EID-CT (32 patients, 50 mL, CTDIvol 5.1 mGy·cm). Pulmonary artery CT attenuation, signal-to-noise ratio, and contrast-to-noise-ratio were assessed as objective criteria of image quality, while subjective ratings of four radiologists were compared at 60 keV using virtual monoenergetic imaging and polychromatic standard reconstructions. Interrater reliability was determined by means of the intraclass correlation coefficient (ICC). Effective dose was compared between patient cohorts. Results: Subjective image quality was deemed superior by all four reviewers for 60-keV PCD scans (excellent or good ratings in 93.8% of PCD vs. 84.4% of 60 keV EID scans, ICC = 0.72). No examinations on either system were considered "non-diagnostic." Objective image quality parameters were significantly higher in the EID group (mostly p < 0.001), both in the polychromatic reconstructions and at 60 keV. The ED (1.4 vs. 3.3 mSv) was significantly lower in the PCD cohort (p < 0.001). Conclusions: PCD-CTPA allows for considerable reduction of contrast medium and radiation dose in the diagnosis of acute pulmonary embolism, while maintaining good to excellent image quality compared to conventional EID-CTPA. Clinical relevance statement: Clinical PCD-CT allows for spectral assessment of pulmonary vasculature with high scan speed, which is beneficial in patients with suspected pulmonary embolism, frequently presenting with dyspnea. Simultaneously PCD-CT enables substantial reduction of contrast medium and radiation dose. Key Points: • The clinical photon-counting detector CT scanner used in this study allows for high-pitch multi-energy acquisitions. • Photon-counting computed tomography allows for considerable reduction of contrast medium and radiation dose in the diagnosis of acute pulmonary embolism. • Subjective image quality was rated best for 60-keV photon-counting scans. [ABSTRACT FROM AUTHOR]

Objective: To determine whether MRI provides improved diagnostic accuracy compared to radiography for the diagnosis of extremity osteomyelitis (OM) with multi-reader analysis. Methods: In this cross-sectional study, three musculoskeletal fellowship-trained expert radiologists evaluated cases of suspected OM in two rounds—first using radiographs (XR), then with conventional MRI. Radiologic features consistent with OM were recorded. Each reader recorded individual findings on both modalities and rendered a binary diagnosis along with certainty of final diagnosis on a confidence scale of 1–5. This was compared with the pathology-proven diagnosis of OM to determine diagnostic performance. Intraclass correlation (ICC) and Conger's Kappa were used for statistics. Results: XR and MRIs of 213 pathology proven cases (51.5 years ± 14.0 years, mean ± St.Dev.) were included in this study, with 79 tested positive for OM and 98 were positive for a soft tissue abscess, with 78 patients being negative for both. In total, 139 were males and 74 females with bones of interest in the upper and lower extremities in 29 and 184 cases, respectively. MRI showed significantly higher sensitivity and negative predictive value than XR (p < 0.001 for both metrics). Conger's Kappa for OM diagnosis were 0.62 and 0.74 on XR and MRI, respectively. Reader confidence improved slightly from 4.54 to 4.57 when MRI was used. Conclusions: MRI is a diagnostically more effective imaging modality than XR for finding extremity osteomyelitis with better inter-reader reliability. Clinical relevance statement: This study validates the diagnosis of OM with MRI over XR but adds novelty because it is the largest study of its kind with a clear reference standard to guide clinician decision making. Key Points: • Radiography is the first-line imaging modality for musculoskeletal pathology but MRI can add value for infections. • MRI shows greater sensitivity for the diagnosis of osteomyelitis of the extremities than radiography. • This improved diagnostic accuracy makes MRI a better imaging modality for patients with suspected osteomyelitis. [ABSTRACT FROM AUTHOR]

Objective: Triple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI. Methods: This retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated. Results: In total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs. Conclusion: A multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs. Clinical relevance statement: Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue. Key Points: • It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic performance of attenuation imaging (ATI) with an ultrasound scanner (US) in the detection of paediatric hepatic steatosis. Methods: Ninety-four prospectively enrolled children were classified into normal weight and overweight/obese (OW/OB) groups according to body mass index (BMI). US findings, including hepatic steatosis grade and ATI value, were examined by two radiologists. Anthropometric and biochemical parameters were obtained, and nonalcoholic fatty liver disease (NAFLD) scores, including the Framingham steatosis index (FSI) and hepatic steatosis index (HSI), were calculated. Results: After screening, 49 OW/OB and 40 normal weight children aged 10–18 years old (55 males and 34 females) participated in this study. The ATI value was significantly higher in the OW/OB group than in the normal weight group and showed a significant positive correlation with BMI, serum alanine transferase (ALT), uric acid, and NAFLD scores (p < 0.05). In the multiple linear regression adjusted for age, sex, BMI, ALT, uric acid, and HSI, ATI showed a significant positive association with BMI and ALT (p < 0.05). The receiver operating characteristic analysis showed a very good ability of ATI to predict hepatic steatosis. The intraclass correlation coefficient (ICC) of interobserver variability was 0.92, and the ICCs of intraobserver variability were 0.96 and 0.93 (p < 0.05). According to the two-level Bayesian latent class model analysis, the diagnostic performance of ATI showed the best performance for predicting hepatic steatosis among other known noninvasive NAFLD predictors. Conclusions: This study suggests that ATI is an objective and possible surrogate screening test for detecting hepatic steatosis in paediatric patients with obesity. Clinical relevance statement: Using ATI as a quantitative tool in hepatic steatosis allows clinicians to estimate the extent of the condition and track changes over time. This is helpful for monitoring disease progression and guiding treatment decisions, especially in paediatric practice. Key Points: • Attenuation imaging is a noninvasive US-based method for the quantification of hepatic steatosis. • Attenuation imaging values were significantly higher in the OW/OB and steatosis groups than in the normal weight and no steatosis groups, respectively, with a meaningful correlation with known clinical indicators of nonalcoholic fatty liver disease. • Attenuation imaging performs better than other noninvasive predictive models used to diagnose hepatic steatosis. [ABSTRACT FROM AUTHOR]

Objectives: To qualitatively and quantitatively compare a single breath-hold fast half-Fourier single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE) with T2-weighted BLADE sequence for liver MRI at 3 T. Methods: From December 2020 to January 2021, patients with liver MRI were prospectively included. For qualitative analysis, sequence quality, presence of artifacts, conspicuity, and presumed nature of the smallest lesion were assessed using the chi-squared and McNemar tests. For quantitative analysis, number of liver lesions, size of the smallest lesion, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in both sequences were assessed using the paired Wilcoxon signed-rank test. Intraclass correlation coefficients (ICCs) and kappa coefficients were used to assess agreement between the two readers. Results: One hundred and twelve patients were evaluated. Overall image quality (p =.006), artifacts (p <.001), and conspicuity of the smallest lesion (p =.001) were significantly better for the DL HASTE sequence than for the T2-weighted BLADE sequence. Significantly more liver lesions were detected with the DL HASTE sequence (356 lesions) than with the T2-weighted BLADE sequence (320 lesions; p <.001). CNR was significantly higher for the DL HASTE sequence (p <.001). SNR was higher for the T2-weighted BLADE sequence (p <.001). Interreader agreement was moderate to excellent depending on the sequence. Of the 41 supernumerary lesions visible only on the DL HASTE sequence, 38 (93%) were true-positives. Conclusion: The DL HASTE sequence can be used to improve image quality and contrast and reduces artifacts, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. Clinical relevance statement: The DL HASTE sequence is superior to the T2-weighted BLADE sequence for the detection of focal liver lesions and can be used in daily practice as a standard sequence. Key Points: • The half-Fourier acquisition single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE sequence) has better overall image quality, reduced artifacts (particularly motion artifacts), and improved contrast, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. • The acquisition time of the DL HASTE sequence is at least eight times faster (21 s) than that of the T2-weighted BLADE sequence (3–5 min). • The DL HASTE sequence could replace the conventional T2-weighted BLADE sequence to meet the growing indication for hepatic MRI in clinical practice, given its diagnostic and time-saving performance. [ABSTRACT FROM AUTHOR]

Objective: Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. Methods: Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R2 value as a measure of goodness of fit. Results: A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R2 decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes. Conclusion: A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels. Clinical relevance statement: Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation. Key Points: • The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. • A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. • A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level. [ABSTRACT FROM AUTHOR]

Objectives: The prognostic value of subventricular zone distance (SVD) is unclear because of different definitions and lack of evaluation of clinical survival models. The aim of this study was to define SVD and evaluate its prognostic value in a survival nomogram for glioblastoma. Methods: This retrospective study included 158 (SVD biomarker) from historical glioblastoma patients and 187 (survival modeling) with IDH-wild type glioblastoma from a prospective registry (NCT02619890). SVD was assessed by two radiologists: definition 1, the distance between the tumor edge to subventricular zone (SVZ); definition 2, the distance between the tumor centroid to SVZ; definition 3, enhancement at the ventricular wall. The associations between SVD and overall survival (OS) were evaluated using multivariable Cox proportional hazards regression analysis. Performance of an updated SVD survival model was compared with that of the Radiation Therapy Oncology Group (RTOG) 0525 nomogram. Results: SVD according to both definition 1 (hazard ratio [HR]: 0.97, 95% CI: 0.94–0.99; p =.011) and definition 2 (HR: 0.96, 0.94–0.98, p <.001) was adversely associated with OS. Definition 1 was adversely associated with PFS (HR: 0.96, 0.94–0.99, p =.008) and showed the highest reproducibility (intraclass correlation coefficient, 0.90). The SVD-updated model showed similar to better performance than the RTOG model for predicting OS of up to 3 years (AUC: 0.735–0.738 vs. 0.687–0.708), with higher time-dependent specificity for 1-year (89.9% vs. 70.6%) and 3-year OS (93.3% vs. 80.0%). Conclusion: SVZ distance is an independent adverse prognostic factor in patients with IDH-wild type glioblastoma. Updating the survival model with SVZ provides better time-dependent specificity and reproducibility. Key Points: • Subventricular zone distance (SVD) measurement from tumor edge showed high reproducibility. • Longer SVD was independently associated with longer overall survival. • Adding SVD improved time-dependent specificity for survival model in a prospective registry. [ABSTRACT FROM AUTHOR]

Objectives: To investigate associations between apparent diffusion coefficient (ADC) and cell count, Ki 67, tumor-stroma ratio (TSR), and tumoral lymphocytes in different hepatic malignancies. Methods: We identified 149 cases with performed liver biopsies: hepatocellular cancer (HCC, n = 53), intrahepatic cholangiocarcinoma (iCC, n = 29), metastases of colorectal cancer (CRC, n = 24), metastases of breast cancer (BC, n = 28), and metastases of pancreatic cancer (PC, n = 15). MRI was performed on a 1.5-T scanner with an axial echo-planar sequence. MRI was done before biopsy. Biopsy images of target lesions were selected. The cylindrical region of interest was placed on the ADC map of target lesions in accordance with the needle position on the biopsy images. Mean ADC values were estimated. TSR, cell counts, proliferation index Ki 67, and number of tumor-infiltrating lymphocytes were estimated. Spearman's rank correlation coefficients and intraclass correlation coefficients were calculated. Results: Inter-reader agreement was excellent regarding the ADC measurements. In HCC, ADC correlated with cell count (r = − 0.68, p < 0.001) and with TSR (r = 0.31, p = 0.024). In iCC, ADC correlated with TSR (r = 0.60, p < 0.001) and with cell count (r = − 0.54, p = 0.002). In CRC metastases, ADC correlated with cell count (r = − 0.54 p = 0.006) and with Ki 67 (r = − 0.46, p = 0.024). In BC liver metastases, ADC correlated with TSR (r = 0.55, p < 0.002) and with Ki 67 (r = − 0.51, p = 0.006). In PC metastases, no significant correlations were found. Conclusions: ADC correlated with tumor cellularity in HCC, iCC, and CRC liver metastases. ADC reflects TSR in BC liver metastases, HCC, and iCC. ADC cannot reflect intratumoral lymphocytes. Clinical relevance statement: The present study shows that the apparent diffusion coefficient can be used as a surrogate imaging marker for different histopathological features in several malignant hepatic lesions. Key Points: • ADC reflects different histopathological features in several hepatic tumors. • ADC correlates with tumor cellularity in HCC, iCC, and CRC metastases. • ADC strongly correlates with tumor-stroma ratio in BC metastases and iCC. [ABSTRACT FROM AUTHOR]

Objectives: The current guidelines contain substantial inconsistency regarding the use of metformin concomitantly with contrast media. The objective of this study is to appraise the guidelines and summarize the agreements and differences among recommendations. Methods: Our search focused on English language guidelines published between 2018 and 2021. Guidelines for the management of contrast media in patients with continuous metformin were included. Guidelines were assessed using the Appraisal of Guidelines for Research and Evaluation II instrument. Results: Six guidelines out of 1134 fulfilled the inclusion criteria with an AGREE II score of 79.2% (IQR 72.7 to 85.1%). There was good overall quality of the guidelines, with six considered "strongly recommended." CPGs scored poorly in "Clarity of Presentation" and "Applicability," with scores of 75.9% and 76.4%, respectively. The intraclass correlation coefficients were excellent in each domain. There are some guidelines (33.3%) that recommend discontinuation of metformin in patients with an eGFR of < 30 mL/min/1.73 m2, while some guidelines (16.7%) suggest the threshold of renal function should be eGFR < 40 mL/min/1.73 m2. Conclusions: Most guidelines recommend withdrawing metformin before using contrast agents in diabetic patients with severely impaired kidney function but disagree on the renal function thresholds. Furthermore, the gaps regarding discontinuing metformin with moderate renal impairment (30 mL/min/1.73 m2 < eGFR < 60 mL/min/1.73 m2) must be considered in future studies. Key Points: • Guidelines involving metformin and contrast agents are reliable and optimal. • Most guidelines advocate discontinuing metformin before using contrast agents in diabetic patients with advanced renal failure, but there are controversial suggestions regarding kidney function thresholds. • The gaps regarding the time of discontinuation of the metformin with moderate renal impairment (30 mL/min/1.73 m2 < eGFR < 60 mL/min/1.73 m2) must be considered in the extensive RCT studies. [ABSTRACT FROM AUTHOR]

Objectives: To explore whether intravoxel incoherent motion (IVIM) parameters could evaluate liver regeneration preoperatively. Methods: A total of 175 HCC patients were initially recruited. The apparent diffusion coefficient, true diffusion coefficient (D), pseudodiffusion coefficient (D*), pseudodiffusion fraction (f), diffusion distribution coefficient, and diffusion heterogeneity index (Alpha) were measured by two independent radiologists. Spearman's correlation test was used to assess correlations between IVIM parameters and the regeneration index (RI), calculated as 100% × (the volume of the postoperative remnant liver − the volume of the preoperative remnant liver) / the volume of the preoperative remnant liver. Multivariate linear regression analyses were used to identify the factors for RI. Results: Finally, 54 HCC patients (45 men and 9 women, mean age 51.26 ± 10.41 years) were retrospectively analyzed. The intraclass correlation coefficient ranged from 0.842 to 0.918. In all patients, fibrosis stage was reclassified as F0–1 (n = 10), F2–3 (n = 26), and F4 (n = 18) using the METAVIR system. Spearman correlation test showed D* (r = 0.303, p = 0.026) was associated with RI; however, multivariate analysis showed that only D value was a significant predictor (p < 0.05) of RI. D and D*showed moderate correlations with fibrosis stage (r = −0.361, p = 0.007; r = −0.457, p = 0.001). Fibrosis stage showed a negative correlation with RI (r = −0.263, p = 0.015). In the 29 patients who underwent minor hepatectomy, only the D value showed a positive association (p < 0.05) with RI, and a negative correlation with fibrosis stage (r = −0.360, p = 0.018). However, in the 25 patients who underwent major hepatectomy, no IVIM parameters were associated with RI (p > 0.05). Conclusions: The D and D* values, especially the D value, may be reliable preoperative predictors of liver regeneration. Key Points: • The D and D* values, especially the D value, derived from IVIM diffusion-weighted imaging may be useful markers for the preoperative prediction of liver regeneration in patients with HCC. • The D and D* values derived from IVIM diffusion-weighted imaging show significant negative correlations with fibrosis, an important predictor of liver regeneration. • No IVIM parameters were associated with liver regeneration in patients who underwent major hepatectomy, but the D value was a significant predictor of liver regeneration in patients who underwent minor hepatectomy. [ABSTRACT FROM AUTHOR]

Objective: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and malignant vertebral compression fractures (VCFs) based on MRI. Methods: We tested a deep-learning system in 123 benign and 86 malignant VCFs. The median sagittal T1-weighted images (T1WI), T2-weighted images with fat suppression (T2WI-FS), and a combination of both (thereafter, T1WI/T2WI-FS) were used to validate TSCCN. The receiver operator characteristic (ROC) curve was analyzed to evaluate the performance of TSCCN. The accuracy, sensitivity, and specificity of TSCCN in differentiating benign and malignant VCFs were calculated and compared with radiologists' assessments. Intraclass correlation coefficients (ICCs) were tested to find intra- and inter-observer agreement of radiologists in differentiating malignant from benign VCFs. Results: The AUC of the ROC plots of TSCCN according to T1WI, T2WI-FS, and T1WI/T2WI-FS images were 99.2%, 91.7%, and 98.2%, respectively. The accuracy of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was 95.2%, 90.4%, and 96.2%, respectively, greater than that achieved by radiologists. Further, the specificity of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was higher at 98.4%, 94.3%, and 99.2% than that achieved by radiologists. The intra- and inter-observer agreements of radiologists were 0.79–0.85 and 0.79–0.80 for T1WI, 0.65–0.72 and 0.70–0.74 for T2WI-FS, and 0.83–0.88 and 0.83–0.84 for T1WI/T2WI-FS. Conclusion: The TSCCN model showed better diagnostic performance than radiologists for automatically identifying benign or malignant VCFs, and is a potentially helpful tool for future clinical application. Clinical relevance statement: TSCCN-assisted MRI has shown superior performance in distinguishing benign and malignant vertebral compression fractures compared to radiologists. This technology has the value to enhance diagnostic accuracy, sensitivity, and specificity. Further integration into clinical practice is required to optimize patient management. Key Points: • The Two-Stream Compare and Contrast Network (TSCCN) model showed better diagnostic performance than radiologists for identifying benign vs malignant vertebral compression fractures. • The processing of TSCCN is fast and stable, better than the subjective evaluation by radiologists in diagnosing vertebral compression fractures. • The TSCCN model provides options for developing a fully automated, streamlined artificial intelligence diagnostic tool. [ABSTRACT FROM AUTHOR]

Objectives: Radiomics image data analysis offers promising approaches in research but has not been implemented in clinical practice yet, partly due to the instability of many parameters. The aim of this study is to evaluate the stability of radiomics analysis on phantom scans with photon-counting detector CT (PCCT). Methods: Photon-counting CT scans of organic phantoms consisting of 4 apples, kiwis, limes, and onions each were performed at 10 mAs, 50 mAs, and 100 mAs with 120-kV tube current. The phantoms were segmented semi-automatically and original radiomics parameters were extracted. This was followed by statistical analysis including concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), as well as random forest (RF) analysis, and cluster analysis to determine the stable and important parameters. Results: Seventy-three of the 104 (70%) extracted features showed excellent stability with a CCC value > 0.9 when compared in a test and retest analysis, and 68 features (65.4%) were stable compared to the original in a rescan after repositioning. Between the test scans with different mAs values, 78 (75%) features were rated with excellent stability. Eight radiomics features were identified that had an ICC value greater than 0.75 in at least 3 of 4 groups when comparing the different phantoms in a phantom group. In addition, the RF analysis identified many features that are important for distinguishing the phantom groups. Conclusion: Radiomics analysis using PCCT data provides high feature stability on organic phantoms, which may facilitate the implementation of radiomics analysis likewise in clinical routine. Key Points: • Radiomics analysis using photon-counting computed tomography provides high feature stability. • Photon-counting computed tomography may pave the way for implementation of radiomics analysis in clinical routine. [ABSTRACT FROM AUTHOR]

Objectives: Bone marrow edema (BME) from dual-energy CT is useful to direct attention to radiographically occult fractures. The aim was to characterize utility of BME of lower extremity (LE) fractures with the hypothesis that stabilized and post-acute fractures exhibit decreased extent and frequency of BME than non-stabilized and acute fractures, respectively. Methods: An IRB-approved retrospective review of known LE fractures. A total of 141 cases met inclusion criteria, including 82 fractures without splint/cast stabilization, and 59 cases with stabilization. Two readers independently recorded BME, and its multiplicity and area (mm2). A separate reader assessed fracture location, comminution, and chronicity. Wilcoxon rank sum test, multiple regression, intraclass correlation (ICC), kappa statistics, and chi-square tests were used. Results: BME was significantly larger in non-stabilized (859.3 mm2 (420.6–1451.8)) than stabilized fractures (493.5 mm2 (288.8–883.2)), p =.011). Comminuted (p = 0.006), non-stabilized (p = 0.0004), and acute fractures (p = 0.036) were all associated with larger BME area. BME presence had excellent results for both stabilized (Cohen's Kappa = 0.81) and non-stabilized fractures (Cohen's Kappa = 0.84). ICC for BME area showed excellent correlation for both stabilized (ICC = 0.78) and non-stabilized groups (ICC = 0.86). BME multiplicity showed excellent agreement for stabilized (ICC = 0.81) and good agreement for non-stabilized (ICC = 0.67) fractures. Lastly, stabilized cases showed increased multiplicity of BME compared to non-stabilized fractures (p < 0.001). Conclusions: BME evaluation can assist in differentiation of acute versus post-acute fractures. Extent of BME is reduced with splint/cast stabilization, which may limit its accuracy in detection of lower extremity fractures. Key Points: • Evaluation of bone marrow edema on dual-energy CT aids in differentiation of acute versus post-acute fracture. • Bone marrow edema evaluation is limited in the setting of post-acute or stabilized fractures. • There is decreased frequency and extent of bone marrow edema in post-acute, non-comminuted, and stabilized fractures. [ABSTRACT FROM AUTHOR]

Objective: To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)–based denoising technique. Methods: This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1–10, 11–100, 101–400, and > 400. We compared CACS from LDCTs with that from calcium CT. Results: Mean CACS was 104.8 ± 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 ± 249.6, p = 0.002), 1-mm LDCT with IR (94.7 ± 249.9, p < 0.001), and 3-mm LDCT (90.3 ± 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945–0.972) for DL, 0.969 (95% CI, 0.956–0.978) for IR, and 0.952 (95% CI, 0.932–0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893–0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866–0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780–0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10). Conclusion: DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel. Key Points: • Deep learning (DL)–based noise reduction enables calcium scoring at 1-mm, sharp kernel reconstructed low-dose chest CT (LDCT). • Both iterative reconstruction and DL-based noise reduction underestimated calcium score, but agreement were excellent with those from calcium CT. • Accuracy of categorical classification of calcium scoring tended to be highest in 1-mm LDCT with DL compared to 1-mm LDCT with IR and 3-mm LDCT (90%, 87%, and 84.7%, p = 0.10). [ABSTRACT FROM AUTHOR]

Objectives: The aim is to validate the performance of a deep convolutional neural network (DCNN) for vertebral body measurements and insufficiency fracture detection on lumbar spine MRI. Methods: This retrospective analysis included 1000 vertebral bodies in 200 patients (age 75.2 ± 9.8 years) who underwent lumbar spine MRI at multiple institutions. 160/200 patients had ≥ one vertebral body insufficiency fracture, 40/200 had no fracture. The performance of the DCNN and that of two fellowship-trained musculoskeletal radiologists in vertebral body measurements (anterior/posterior height, extent of endplate concavity, vertebral angle) and evaluation for insufficiency fractures were compared. Statistics included (a) interobserver reliability metrics using intraclass correlation coefficient (ICC), kappa statistics, and Bland-Altman analysis, and (b) diagnostic performance metrics (sensitivity, specificity, accuracy). A statistically significant difference was accepted if the 95% confidence intervals did not overlap. Results: The inter-reader agreement between radiologists and the DCNN was excellent for vertebral body measurements, with ICC values of > 0.94 for anterior and posterior vertebral height and vertebral angle, and good to excellent for superior and inferior endplate concavity with ICC values of 0.79–0.85. The performance of the DCNN in fracture detection yielded a sensitivity of 0.941 (0.903–0.968), specificity of 0.969 (0.954–0.980), and accuracy of 0.962 (0.948–0.973). The diagnostic performance of the DCNN was independent of the radiological institution (accuracy 0.964 vs. 0.960), type of MRI scanner (accuracy 0.957 vs. 0.964), and magnetic field strength (accuracy 0.966 vs. 0.957). Conclusions: A DCNN can achieve high diagnostic performance in vertebral body measurements and insufficiency fracture detection on heterogeneous lumbar spine MRI. Key Points: • A DCNN has the potential for high diagnostic performance in measuring vertebral bodies and detecting insufficiency fractures of the lumbar spine. [ABSTRACT FROM AUTHOR]

Objectives: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. Methods: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2–4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: Pre-training ICCs of the four morphologic features ranged from 0.04 (−0.05 to 0.13) to 0.68 (0.49–0.81) between observer 1 and observers 2–4 and from 0.50 (0.32–0.69) to 0.89 (0.78–0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52–0.87) to 0.97 (0.94–0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. Conclusions: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. Key Points: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models. [ABSTRACT FROM AUTHOR]

Objectives: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student’s paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (−19.5 ± 4.3 vs −19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.QuestionCine CMR acquisitions are time-intensive and vulnerable to artifacts.FindingsLow-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35–42% without a significant difference in volumetric results or subjective image quality.Clinical relevanceDL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.Materials and methods: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student’s paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (−19.5 ± 4.3 vs −19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.QuestionCine CMR acquisitions are time-intensive and vulnerable to artifacts.FindingsLow-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35–42% without a significant difference in volumetric results or subjective image quality.Clinical relevanceDL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.Results: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student’s paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (−19.5 ± 4.3 vs −19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.QuestionCine CMR acquisitions are time-intensive and vulnerable to artifacts.FindingsLow-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35–42% without a significant difference in volumetric results or subjective image quality.Clinical relevanceDL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.Conclusion: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student’s paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (−19.5 ± 4.3 vs −19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.QuestionCine CMR acquisitions are time-intensive and vulnerable to artifacts.FindingsLow-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35–42% without a significant difference in volumetric results or subjective image quality.Clinical relevanceDL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.Key Points: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student’s paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (−19.5 ± 4.3 vs −19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.QuestionCine CMR acquisitions are time-intensive and vulnerable to artifacts.FindingsLow-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35–42% without a significant difference in volumetric results or subjective image quality.Clinical relevanceDL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.Graphical Abstract: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student’s paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis.Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (−19.5 ± 4.3 vs −19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99).Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality.QuestionCine CMR acquisitions are time-intensive and vulnerable to artifacts.FindingsLow-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35–42% without a significant difference in volumetric results or subjective image quality.Clinical relevanceDL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA).Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photon-counting detector CCTA were included. Images were reconstructed with a quantitative smooth (Qr36) and three vascular kernels of increasing sharpness levels (Bv36, Bv44, Bv56). Quantum iterative reconstruction (QIR) was either switched-off (QIRoff) or was used with strength levels 2 and 4. The fat-attenuation-index (FAI) of the PCAT surrounding the right coronary artery was calculated in each dataset. Histograms of FAI measurements were created. Intra- and inter-reader agreements were determined. A CT edge phantom was used to determine the edge spread function (ESF) for the same datasets.Intra- and inter-reader agreement of FAI was excellent (intra-class correlation coefficient = 0.99 and 0.98, respectively). Significant differences in FAI were observed depending on the kernel and iterative reconstruction strength level (each, p < 0.001), with considerable inter-individual variation up to 34 HU and intra-individual variation up to 33 HU, depending on kernels and iterative reconstruction levels. The ESFs showed a reduced range of edge-smoothing with increasing kernel sharpness, causing an FAI decrease. Histogram analyses revealed a narrower peak of PCAT values with increasing iterative reconstruction levels, causing a FAI increase.PCAT attenuation determined with CCTA heavily depends on kernels and iterative reconstruction levels both within and across subjects. Standardization of CT reconstruction parameters is mandatory for FAI studies to enable meaningful interpretations.QuestionDo kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA)?FindingsSignificant differences in fat-attenuation-index (FAI) were observed depending on the kernel and iterative reconstruction strength level with considerable inter- and intra-individual variation.Clinical relevancePCAT attenuation heavily depends on kernels and iterative reconstructions requiring CT reconstruction parameter standardization to enable meaningful interpretations of fat-attenuation differences across subjects.Materials and methods: To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA).Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photon-counting detector CCTA were included. Images were reconstructed with a quantitative smooth (Qr36) and three vascular kernels of increasing sharpness levels (Bv36, Bv44, Bv56). Quantum iterative reconstruction (QIR) was either switched-off (QIRoff) or was used with strength levels 2 and 4. The fat-attenuation-index (FAI) of the PCAT surrounding the right coronary artery was calculated in each dataset. Histograms of FAI measurements were created. Intra- and inter-reader agreements were determined. A CT edge phantom was used to determine the edge spread function (ESF) for the same datasets.Intra- and inter-reader agreement of FAI was excellent (intra-class correlation coefficient = 0.99 and 0.98, respectively). Significant differences in FAI were observed depending on the kernel and iterative reconstruction strength level (each, p < 0.001), with considerable inter-individual variation up to 34 HU and intra-individual variation up to 33 HU, depending on kernels and iterative reconstruction levels. The ESFs showed a reduced range of edge-smoothing with increasing kernel sharpness, causing an FAI decrease. Histogram analyses revealed a narrower peak of PCAT values with increasing iterative reconstruction levels, causing a FAI increase.PCAT attenuation determined with CCTA heavily depends on kernels and iterative reconstruction levels both within and across subjects. Standardization of CT reconstruction parameters is mandatory for FAI studies to enable meaningful interpretations.QuestionDo kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA)?FindingsSignificant differences in fat-attenuation-index (FAI) were observed depending on the kernel and iterative reconstruction strength level with considerable inter- and intra-individual variation.Clinical relevancePCAT attenuation heavily depends on kernels and iterative reconstructions requiring CT reconstruction parameter standardization to enable meaningful interpretations of fat-attenuation differences across subjects.Results: To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA).Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photon-counting detector CCTA were included. Images were reconstructed with a quantitative smooth (Qr36) and three vascular kernels of increasing sharpness levels (Bv36, Bv44, Bv56). Quantum iterative reconstruction (QIR) was either switched-off (QIRoff) or was used with strength levels 2 and 4. The fat-attenuation-index (FAI) of the PCAT surrounding the right coronary artery was calculated in each dataset. Histograms of FAI measurements were created. Intra- and inter-reader agreements were determined. A CT edge phantom was used to determine the edge spread function (ESF) for the same datasets.Intra- and inter-reader agreement of FAI was excellent (intra-class correlation coefficient = 0.99 and 0.98, respectively). Significant differences in FAI were observed depending on the kernel and iterative reconstruction strength level (each, p < 0.001), with considerable inter-individual variation up to 34 HU and intra-individual variation up to 33 HU, depending on kernels and iterative reconstruction levels. The ESFs showed a reduced range of edge-smoothing with increasing kernel sharpness, causing an FAI decrease. Histogram analyses revealed a narrower peak of PCAT values with increasing iterative reconstruction levels, causing a FAI increase.PCAT attenuation determined with CCTA heavily depends on kernels and iterative reconstruction levels both within and across subjects. Standardization of CT reconstruction parameters is mandatory for FAI studies to enable meaningful interpretations.QuestionDo kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA)?FindingsSignificant differences in fat-attenuation-index (FAI) were observed depending on the kernel and iterative reconstruction strength level with considerable inter- and intra-individual variation.Clinical relevancePCAT attenuation heavily depends on kernels and iterative reconstructions requiring CT reconstruction parameter standardization to enable meaningful interpretations of fat-attenuation differences across subjects.Conclusions: To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA).Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photon-counting detector CCTA were included. Images were reconstructed with a quantitative smooth (Qr36) and three vascular kernels of increasing sharpness levels (Bv36, Bv44, Bv56). Quantum iterative reconstruction (QIR) was either switched-off (QIRoff) or was used with strength levels 2 and 4. The fat-attenuation-index (FAI) of the PCAT surrounding the right coronary artery was calculated in each dataset. Histograms of FAI measurements were created. Intra- and inter-reader agreements were determined. A CT edge phantom was used to determine the edge spread function (ESF) for the same datasets.Intra- and inter-reader agreement of FAI was excellent (intra-class correlation coefficient = 0.99 and 0.98, respectively). Significant differences in FAI were observed depending on the kernel and iterative reconstruction strength level (each, p < 0.001), with considerable inter-individual variation up to 34 HU and intra-individual variation up to 33 HU, depending on kernels and iterative reconstruction levels. The ESFs showed a reduced range of edge-smoothing with increasing kernel sharpness, causing an FAI decrease. Histogram analyses revealed a narrower peak of PCAT values with increasing iterative reconstruction levels, causing a FAI increase.PCAT attenuation determined with CCTA heavily depends on kernels and iterative reconstruction levels both within and across subjects. Standardization of CT reconstruction parameters is mandatory for FAI studies to enable meaningful interpretations.QuestionDo kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA)?FindingsSignificant differences in fat-attenuation-index (FAI) were observed depending on the kernel and iterative reconstruction strength level with considerable inter- and intra-individual variation.Clinical relevancePCAT attenuation heavily depends on kernels and iterative reconstructions requiring CT reconstruction parameter standardization to enable meaningful interpretations of fat-attenuation differences across subjects.Key Points: To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA).Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photon-counting detector CCTA were included. Images were reconstructed with a quantitative smooth (Qr36) and three vascular kernels of increasing sharpness levels (Bv36, Bv44, Bv56). Quantum iterative reconstruction (QIR) was either switched-off (QIRoff) or was used with strength levels 2 and 4. The fat-attenuation-index (FAI) of the PCAT surrounding the right coronary artery was calculated in each dataset. Histograms of FAI measurements were created. Intra- and inter-reader agreements were determined. A CT edge phantom was used to determine the edge spread function (ESF) for the same datasets.Intra- and inter-reader agreement of FAI was excellent (intra-class correlation coefficient = 0.99 and 0.98, respectively). Significant differences in FAI were observed depending on the kernel and iterative reconstruction strength level (each, p < 0.001), with considerable inter-individual variation up to 34 HU and intra-individual variation up to 33 HU, depending on kernels and iterative reconstruction levels. The ESFs showed a reduced range of edge-smoothing with increasing kernel sharpness, causing an FAI decrease. Histogram analyses revealed a narrower peak of PCAT values with increasing iterative reconstruction levels, causing a FAI increase.PCAT attenuation determined with CCTA heavily depends on kernels and iterative reconstruction levels both within and across subjects. Standardization of CT reconstruction parameters is mandatory for FAI studies to enable meaningful interpretations.QuestionDo kernels and iterative reconstructions influence pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA)?FindingsSignificant differences in fat-attenuation-index (FAI) were observed depending on the kernel and iterative reconstruction strength level with considerable inter- and intra-individual variation.Clinical relevancePCAT attenuation heavily depends on kernels and iterative reconstructions requiring CT reconstruction parameter standardization to enable meaningful interpretations of fat-attenuation differences across subjects. [ABSTRACT FROM AUTHOR]

Objectives: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland–Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50–64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland–Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS −0.04, −52.5 to 52.4, VS −0.44, −46.51 to 45.63, and MS −0.07, −9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.QuestionCan AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice?FindingsThis study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system.Clinical relevanceThis study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.Materials and methods: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland–Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50–64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland–Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS −0.04, −52.5 to 52.4, VS −0.44, −46.51 to 45.63, and MS −0.07, −9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.QuestionCan AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice?FindingsThis study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system.Clinical relevanceThis study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.Results: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland–Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50–64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland–Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS −0.04, −52.5 to 52.4, VS −0.44, −46.51 to 45.63, and MS −0.07, −9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.QuestionCan AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice?FindingsThis study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system.Clinical relevanceThis study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.Conclusion: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland–Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50–64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland–Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS −0.04, −52.5 to 52.4, VS −0.44, −46.51 to 45.63, and MS −0.07, −9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.QuestionCan AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice?FindingsThis study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system.Clinical relevanceThis study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice.Key Points: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).In total, 5057 CSCT examinations were performed on one CT system at Linköping University Hospital between October 8, 2015, and June 12, 2018. AI evaluations were compared to semi-automatic CSCT results from expert reader evaluations rendered within SCAPIS. Pearson correlation, intraclass correlation coefficients (ICC), and Bland–Altman analysis were applied for Agatston (AS), volume (VS), mass scores (MS), number of lesions and lesion location. Agreement of Agatston score classifications into cardiovascular (CV) risk categories was evaluated with weighted kappa analysis.The evaluation included 4567 subjects, 2229 (48.8%) male, 2338 (51.2%) female, 50–64 years of age (mean 57.3 ± 4.4). The AS ranged from 0 to 2871 in the cohort, with 2846 subjects having an AS of 0. Mean and median AS were 51.4 and 0.0, respectively. Total AS, VS, MS and number of lesions ICCs were 0.994, 0.994, 0.994, 0.960 (p < 0.001), respectively. Bland–Altman analyses rendered mean differences ± 1.96 SD upper and lower limits of agreement for AS −0.04, −52.5 to 52.4, VS −0.44, −46.51 to 45.63, and MS −0.07, −9.62 to 9.48. Weighted kappa analysis for CV risk category classifications was 0.913, and overall accuracy was 91.2%.There was excellent correlation and agreement between AI and semi-automatic evaluations for all calcium scores, number of lesions and lesion location. High degrees of agreement and accuracy were found for the CV risk categorization.QuestionCan AI function as a tool for enhancing the efficiency and accuracy of Coronary Artery Calcium Score (CACS) evaluations in clinical radiology practice?FindingsThis study confirms the robustness of AI-derived CACS results across extensive datasets, though its generalizability is limited by data acquisition from a single CT system.Clinical relevanceThis study suggests that AI holds significant promise as a tool for enhancing the efficiency and accuracy of CACS evaluations, with implications for improving patient diagnostics and reducing radiologist workload in clinical practice. [ABSTRACT FROM AUTHOR]

Objective: To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard.This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis.A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5–18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0–18.0) and 9.0% (IQR: 5.0–18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934–0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852–0.930). The Bland–Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, −8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively.UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.QuestionIs ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)?FindingsUDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.Clinical relevanceUDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.Methods: To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard.This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis.A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5–18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0–18.0) and 9.0% (IQR: 5.0–18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934–0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852–0.930). The Bland–Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, −8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively.UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.QuestionIs ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)?FindingsUDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.Clinical relevanceUDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.Results: To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard.This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis.A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5–18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0–18.0) and 9.0% (IQR: 5.0–18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934–0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852–0.930). The Bland–Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, −8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively.UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.QuestionIs ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)?FindingsUDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.Clinical relevanceUDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.Conclusions: To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard.This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis.A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5–18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0–18.0) and 9.0% (IQR: 5.0–18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934–0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852–0.930). The Bland–Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, −8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively.UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.QuestionIs ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)?FindingsUDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.Clinical relevanceUDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers.Key Points: To compare the agreement between ultrasound-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for quantification of hepatic steatosis and verify its reliability and diagnostic performance by comparing with MRI-PDFF as the reference standard.This prospective study included a primary analysis of 191 patients who underwent MRI-PDFF and UDFF from February 2023 to February 2024. MRI-PDFF were derived from three liver segment measurements with calculation of an overall median PDFF. UDFF was performed by two different sonographers for each of the six measurements, and the median was taken. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were used to assess agreement. Receiver operating characteristics (ROC) curves were used to evaluate the diagnostic performance of UDFF in detecting different degrees of hepatic steatosis.A total of 176 participants were enrolled in the final cohort of this study (median age, 36.0 years; 82 men, 94 women). The median MRI-PDFF value was 11.3% (interquartile range (IQR) 7.5–18.9); 84.7% patients had a median MRI-PDFF value ≥ 6.4%. The median UDFF measured by different sonographers were 9.5% (IQR: 5.0–18.0) and 9.0% (IQR: 5.0–18.0), respectively. The interobserver agreement of UDFF measurement was excellent agreement (ICC = 0.951 [95% CI: 0.934–0.964], p < 0.001). UDFF was positively strongly correlated with MRI-PDFF with ICC of 0.899 (95% CI: 0.852–0.930). The Bland–Altman analysis showed high agreement between UDFF and MRI-PDFF measurements, with a mean bias of 1.7% (95% LOA, −8.7 to 12.2%). The optimal UDFF cutoff values were 5.5%, 15.5% and 17.5% for detecting MRI-PDFF at historic thresholds of 6.4%, 17.4%, and 22.1%, with AUC of 0.851, 0.952, and 0.948, respectively. The sensitivity was 79.2%, 87.5%, 88.9%, and specificity was 81.5%, 90.6%, 90.0%, respectively.UDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF. The UDFF cutoff values of 5.5%, 15.5%, and 17.5% provide high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.QuestionIs ultrasound-derived fat fraction (UDFF) reliable for the quantitative detection of hepatic steatosis compared to MRI proton density fat fraction (MRI-PDFF)?FindingsUDFF cutoff values of 5.5%, 15.5%, and 17.5% provided high sensitivity and specificity for the detection of mild, moderate, and severe hepatic steatosis, respectively.Clinical relevanceUDFF is a reliable and accurate method for quantification and classification of hepatic steatosis, with strong agreement to MRI-PDFF and high reproducibility of liver fat content by different sonographers. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840–0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756–0.919) and 0.914 (95% CI: 0.855–0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9–3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.QuestionIs ChatGPT effective and reliable in evaluating radiomics research quality based on RQS?FindingsChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment.Clinical relevanceChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.Methods: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840–0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756–0.919) and 0.914 (95% CI: 0.855–0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9–3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.QuestionIs ChatGPT effective and reliable in evaluating radiomics research quality based on RQS?FindingsChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment.Clinical relevanceChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.Results: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840–0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756–0.919) and 0.914 (95% CI: 0.855–0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9–3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.QuestionIs ChatGPT effective and reliable in evaluating radiomics research quality based on RQS?FindingsChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment.Clinical relevanceChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.Conclusion: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840–0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756–0.919) and 0.914 (95% CI: 0.855–0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9–3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.QuestionIs ChatGPT effective and reliable in evaluating radiomics research quality based on RQS?FindingsChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment.Clinical relevanceChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.Key Points: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840–0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756–0.919) and 0.914 (95% CI: 0.855–0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9–3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.QuestionIs ChatGPT effective and reliable in evaluating radiomics research quality based on RQS?FindingsChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment.Clinical relevanceChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future.Graphical Abstract: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias. Using the RQS, each study was independently assessed twice by ChatGPT-4o and by two radiologists with consensus.In total, 52 open-access and peer-reviewed articles were included in this study. Both ChatGPT-4o evaluation (average of two readings) and human experts had a median RQS of 14.5 (40.3% percentage score) (p > 0.05). Pairwise comparisons revealed no statistically significant difference between the readings of ChatGPT and human experts (corrected p > 0.05). The intraclass correlation coefficient for intra-rater reliability of ChatGPT-4o was 0.905 (95% CI: 0.840–0.944), and those for inter-rater reliability with human experts for each evaluation of ChatGPT-4o were 0.859 (95% CI: 0.756–0.919) and 0.914 (95% CI: 0.855–0.949), corresponding to good to excellent reliability for all. The evaluation by ChatGPT-4o took less time (2.9–3.5 min per article) compared to human experts (13.9 min per article by one reader). Item-wise reliability analysis showed ChatGPT-4o maintained consistently high reliability across almost all RQS items.ChatGPT-4o provides reliable and efficient assessments of radiomics research quality. Its evaluations closely align with those of human experts and reduce evaluation time.QuestionIs ChatGPT effective and reliable in evaluating radiomics research quality based on RQS?FindingsChatGPT-4o showed high reliability and efficiency, with evaluations closely matching human experts. It can considerably reduce the time required for radiomics research quality assessment.Clinical relevanceChatGPT-4o offers a quick and reliable automated alternative for evaluating the quality of radiomics research, with the potential to assess radiomics research at a large scale in the future. [ABSTRACT FROM AUTHOR]

Purpose: To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2). Hepatic steatosis was graded by liver biopsy as: (0) < 5%; (1) 5–33%; (2) > 33–66%; (3) > 66%. Areas under the curve (AUCs) were calculated to determine the diagnostic performance. Inter- and intra-observer reliability was assessed with intraclass correlation coefficient (ICC).A hundred patients (median age 55.0 years old) with MASLD were prospectively enrolled. At histopathology, 70 and 42 patients had grade ≥ 2 and 3 steatosis, respectively. Median UGAP was 0.78 dB/cm/MHz (IQR/Med: 5.55%). For the diagnosis of grade ≥ 2 steatosis, the AUCs of UGAP were 0.828 (95% CI: 0.739, 0.896) for R1 and 0.779 (95% CI: 0.685, 0.856) for R2. The inter- and intra-operator reliability of UGAP were excellent, with an ICC of 0.92 (95% CI: 0.87–0.95) and 0.95 (95% CI: 0.92–0.96), respectively. The median liver stiffness was 6.76 kPa (IQR/Med: 16.30%). For the diagnosis of advanced fibrosis, 2D-SWE had an AUC of 0.862 (95% CI: 0.757, 0.934), and the optimal cutoff value was > 6.75 kPa with a sensitivity of 80.6% and a specificity of 75.7%.UGAP and 2D-SWE provide a good performance for the staging of steatosis and fibrosis in patients with MASLD with an excellent intra-operator reliability of UGAP.QuestionHow well do ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) perform for quantifying hepatic steatosis and fibrosis?FindingsUGAP had a maximum AUC of 0.828 for the diagnosis of grade ≥ 2 steatosis, and 2D-SWE had an AUC of 0.862 for diagnosing advanced fibrosis.Clinical relevanceUGAP and 2D-SWE allow rapid, reproducible, and accurate quantification of hepatic steatosis and fibrosis that can be used for the noninvasive assessment of patients with metabolic dysfunction-associated steatotic liver disease.Methods: To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2). Hepatic steatosis was graded by liver biopsy as: (0) < 5%; (1) 5–33%; (2) > 33–66%; (3) > 66%. Areas under the curve (AUCs) were calculated to determine the diagnostic performance. Inter- and intra-observer reliability was assessed with intraclass correlation coefficient (ICC).A hundred patients (median age 55.0 years old) with MASLD were prospectively enrolled. At histopathology, 70 and 42 patients had grade ≥ 2 and 3 steatosis, respectively. Median UGAP was 0.78 dB/cm/MHz (IQR/Med: 5.55%). For the diagnosis of grade ≥ 2 steatosis, the AUCs of UGAP were 0.828 (95% CI: 0.739, 0.896) for R1 and 0.779 (95% CI: 0.685, 0.856) for R2. The inter- and intra-operator reliability of UGAP were excellent, with an ICC of 0.92 (95% CI: 0.87–0.95) and 0.95 (95% CI: 0.92–0.96), respectively. The median liver stiffness was 6.76 kPa (IQR/Med: 16.30%). For the diagnosis of advanced fibrosis, 2D-SWE had an AUC of 0.862 (95% CI: 0.757, 0.934), and the optimal cutoff value was > 6.75 kPa with a sensitivity of 80.6% and a specificity of 75.7%.UGAP and 2D-SWE provide a good performance for the staging of steatosis and fibrosis in patients with MASLD with an excellent intra-operator reliability of UGAP.QuestionHow well do ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) perform for quantifying hepatic steatosis and fibrosis?FindingsUGAP had a maximum AUC of 0.828 for the diagnosis of grade ≥ 2 steatosis, and 2D-SWE had an AUC of 0.862 for diagnosing advanced fibrosis.Clinical relevanceUGAP and 2D-SWE allow rapid, reproducible, and accurate quantification of hepatic steatosis and fibrosis that can be used for the noninvasive assessment of patients with metabolic dysfunction-associated steatotic liver disease.Results: To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2). Hepatic steatosis was graded by liver biopsy as: (0) < 5%; (1) 5–33%; (2) > 33–66%; (3) > 66%. Areas under the curve (AUCs) were calculated to determine the diagnostic performance. Inter- and intra-observer reliability was assessed with intraclass correlation coefficient (ICC).A hundred patients (median age 55.0 years old) with MASLD were prospectively enrolled. At histopathology, 70 and 42 patients had grade ≥ 2 and 3 steatosis, respectively. Median UGAP was 0.78 dB/cm/MHz (IQR/Med: 5.55%). For the diagnosis of grade ≥ 2 steatosis, the AUCs of UGAP were 0.828 (95% CI: 0.739, 0.896) for R1 and 0.779 (95% CI: 0.685, 0.856) for R2. The inter- and intra-operator reliability of UGAP were excellent, with an ICC of 0.92 (95% CI: 0.87–0.95) and 0.95 (95% CI: 0.92–0.96), respectively. The median liver stiffness was 6.76 kPa (IQR/Med: 16.30%). For the diagnosis of advanced fibrosis, 2D-SWE had an AUC of 0.862 (95% CI: 0.757, 0.934), and the optimal cutoff value was > 6.75 kPa with a sensitivity of 80.6% and a specificity of 75.7%.UGAP and 2D-SWE provide a good performance for the staging of steatosis and fibrosis in patients with MASLD with an excellent intra-operator reliability of UGAP.QuestionHow well do ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) perform for quantifying hepatic steatosis and fibrosis?FindingsUGAP had a maximum AUC of 0.828 for the diagnosis of grade ≥ 2 steatosis, and 2D-SWE had an AUC of 0.862 for diagnosing advanced fibrosis.Clinical relevanceUGAP and 2D-SWE allow rapid, reproducible, and accurate quantification of hepatic steatosis and fibrosis that can be used for the noninvasive assessment of patients with metabolic dysfunction-associated steatotic liver disease.Conclusion: To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2). Hepatic steatosis was graded by liver biopsy as: (0) < 5%; (1) 5–33%; (2) > 33–66%; (3) > 66%. Areas under the curve (AUCs) were calculated to determine the diagnostic performance. Inter- and intra-observer reliability was assessed with intraclass correlation coefficient (ICC).A hundred patients (median age 55.0 years old) with MASLD were prospectively enrolled. At histopathology, 70 and 42 patients had grade ≥ 2 and 3 steatosis, respectively. Median UGAP was 0.78 dB/cm/MHz (IQR/Med: 5.55%). For the diagnosis of grade ≥ 2 steatosis, the AUCs of UGAP were 0.828 (95% CI: 0.739, 0.896) for R1 and 0.779 (95% CI: 0.685, 0.856) for R2. The inter- and intra-operator reliability of UGAP were excellent, with an ICC of 0.92 (95% CI: 0.87–0.95) and 0.95 (95% CI: 0.92–0.96), respectively. The median liver stiffness was 6.76 kPa (IQR/Med: 16.30%). For the diagnosis of advanced fibrosis, 2D-SWE had an AUC of 0.862 (95% CI: 0.757, 0.934), and the optimal cutoff value was > 6.75 kPa with a sensitivity of 80.6% and a specificity of 75.7%.UGAP and 2D-SWE provide a good performance for the staging of steatosis and fibrosis in patients with MASLD with an excellent intra-operator reliability of UGAP.QuestionHow well do ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) perform for quantifying hepatic steatosis and fibrosis?FindingsUGAP had a maximum AUC of 0.828 for the diagnosis of grade ≥ 2 steatosis, and 2D-SWE had an AUC of 0.862 for diagnosing advanced fibrosis.Clinical relevanceUGAP and 2D-SWE allow rapid, reproducible, and accurate quantification of hepatic steatosis and fibrosis that can be used for the noninvasive assessment of patients with metabolic dysfunction-associated steatotic liver disease.Key Points: To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2). Hepatic steatosis was graded by liver biopsy as: (0) < 5%; (1) 5–33%; (2) > 33–66%; (3) > 66%. Areas under the curve (AUCs) were calculated to determine the diagnostic performance. Inter- and intra-observer reliability was assessed with intraclass correlation coefficient (ICC).A hundred patients (median age 55.0 years old) with MASLD were prospectively enrolled. At histopathology, 70 and 42 patients had grade ≥ 2 and 3 steatosis, respectively. Median UGAP was 0.78 dB/cm/MHz (IQR/Med: 5.55%). For the diagnosis of grade ≥ 2 steatosis, the AUCs of UGAP were 0.828 (95% CI: 0.739, 0.896) for R1 and 0.779 (95% CI: 0.685, 0.856) for R2. The inter- and intra-operator reliability of UGAP were excellent, with an ICC of 0.92 (95% CI: 0.87–0.95) and 0.95 (95% CI: 0.92–0.96), respectively. The median liver stiffness was 6.76 kPa (IQR/Med: 16.30%). For the diagnosis of advanced fibrosis, 2D-SWE had an AUC of 0.862 (95% CI: 0.757, 0.934), and the optimal cutoff value was > 6.75 kPa with a sensitivity of 80.6% and a specificity of 75.7%.UGAP and 2D-SWE provide a good performance for the staging of steatosis and fibrosis in patients with MASLD with an excellent intra-operator reliability of UGAP.QuestionHow well do ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) perform for quantifying hepatic steatosis and fibrosis?FindingsUGAP had a maximum AUC of 0.828 for the diagnosis of grade ≥ 2 steatosis, and 2D-SWE had an AUC of 0.862 for diagnosing advanced fibrosis.Clinical relevanceUGAP and 2D-SWE allow rapid, reproducible, and accurate quantification of hepatic steatosis and fibrosis that can be used for the noninvasive assessment of patients with metabolic dysfunction-associated steatotic liver disease. [ABSTRACT FROM AUTHOR]

Objective: To determine if artificial intelligence–based modification of the Thyroid Imaging Reporting Data System (TI-RADS) would be better than the current American College of Radiology (ACR) TI-RADS for risk stratification of thyroid nodules. Methods: A total of 2061 thyroid nodules (in 1859 patients) sampled with fine-needle aspiration or operation were retrospectively analyzed between January 2017 and July 2020. Two radiologists blinded to the pathologic diagnosis evaluated nodule features in five ultrasound categories and assigned TI-RADS scores by both ACR TI-RADS and AI TI-RADS. Inter-rater agreement was assessed by asking another two radiologists to score a set of 100 nodules independently. The reference standard was postoperative pathological or cytopathological diagnosis according to the Bethesda system. Inter-rater agreement was determined using intraclass correlation coefficient (ICC). Results: AI TI-RADS assigned lower TI-RADS risk levels than ACR TI-RADS (p < 0.001) and had larger area under receiver operating characteristic curve (0.762 vs. 0.679, p < 0.001). The sensitivities of ACR TI-RADS and AI TI-RADS were similar (86.7% vs. 82.2%, p = 0.052), but specificity was higher with AI TI-RADS (70.2% vs. 49.2%, p < 0.001). AI TI-RADS downgraded 743 (48.63%) benign nodules, indicating that 328 (42.3% of 776 biopsied nodules) unnecessary fine-needle aspirations (FNA) could have been avoided. Inter-rater agreement was better with AI TI-RADS than with ACR TI-RADS (ICC, 0.808 vs. 0.861, p < 0.001). Conclusion: AI TI-RADS can achieve meaningful reduction in the number of benign thyroid nodules recommended for biopsy and significantly improve specificity despite a slight decrease in sensitivity. Key Points: • AI TI-RADS assigned lower TI-RADS risk levels than ACR TI-RADS, showing similar sensitivity but higher specificity. • Half of the benign nodules can be downgraded of which 42.3% of biopsy nodules avoided unnecessary fine-needle aspiration (FNA). • AI TI-RADS had a better overall inter-rater agreement. [ABSTRACT FROM AUTHOR]

Objectives: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3–107 mm] part-solid n = 6 [ground glass/solid 8 mm/4–28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Materials and methods: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3–107 mm] part-solid n = 6 [ground glass/solid 8 mm/4–28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Results: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3–107 mm] part-solid n = 6 [ground glass/solid 8 mm/4–28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Conclusion: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3–107 mm] part-solid n = 6 [ground glass/solid 8 mm/4–28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Clinical relevance statement: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3–107 mm] part-solid n = 6 [ground glass/solid 8 mm/4–28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Key Points: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3–107 mm] part-solid n = 6 [ground glass/solid 8 mm/4–28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.Lung cancer screening by MRI may be possible but would benefit from scan time optimisation.Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors.Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality. [ABSTRACT FROM AUTHOR]

Objectives: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Materials and methods: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Results: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Conclusion: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Clinical relevance statement: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Key Points: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Graphical Abstract: Holistic segmentation of CT structural alterations with 3D deep learning has recently been described in cystic fibrosis (CF), allowing the measurement of normalized volumes of airway abnormalities (NOVAA-CT) as an automated quantitative outcome. Clinical validations are needed, including longitudinal and multicenter evaluations.The validation study was retrospective between 2010 and 2023. CF patients undergoing Elexacaftor/Tezacaftor/Ivacaftor (ETI) or corticosteroids for allergic broncho-pulmonary aspergillosis (ABPA) composed the monocenter ETI and ABPA groups, respectively. Patients from six geographically distinct institutions composed a multicenter external group. All patients had completed CT and pulmonary function test (PFT), with a second assessment at 1 year in case of ETI or ABPA treatment. NOVAA-CT quantified bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, collapse/consolidation, and their overall total abnormal volume (TAV). Two observers evaluated the visual Bhalla score.A total of 139 CF patients (median age, 15 years [interquartile range: 13–25]) were evaluated. All correlations between NOVAA-CT to both PFT and Bhalla score were significant in the ETI (n = 60), ABPA (n = 20), and External groups (n = 59), such as the normalized TAV (ρ ≥ 0.76; p < 0.001). In both ETI and ABPA groups, there were significant longitudinal improvements in peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation (p ≤ 0.001). An additional reversibility in bronchiectasis volume was quantified with ETI (p < 0.001). Intraclass correlation coefficient of reproducibility was > 0.99.NOVAA-CT automated scoring demonstrates validity, reliability and responsiveness for monitoring CF severity over an entire lung and quantifies therapeutic effects on lung structure at CT, such as the volumetric reversibility of airway abnormalities with ETI.Normalized volume of airway abnormalities at CT automated 3D outcome enables objective, reproducible, and holistic monitoring of cystic fibrosis severity over an entire lung for management and endpoints during therapeutic trials.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects.Visual scoring methods lack sensitivity and reproducibility to assess longitudinal bronchial changes in cystic fibrosis (CF).AI-driven volumetric CT scoring correlates longitudinally to disease severity and reliably improves with Elexacaftor/Tezacaftor/Ivacaftor or corticosteroid treatments.AI-driven volumetric CT scoring enables reproducible monitoring of lung disease severity in CF and quantifies longitudinal structural therapeutic effects. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population.This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC).A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0–14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists.The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses.Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Materials and methods: To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population.This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC).A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0–14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists.The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses.Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Results: To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population.This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC).A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0–14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists.The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses.Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Conclusion: To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population.This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC).A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0–14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists.The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses.Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Clinical relevance statement: To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population.This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC).A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0–14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists.The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses.Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Key Points: To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population.This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC).A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0–14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists.The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses.Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS.Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments.Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone.Junior radiologists’ diagnostic performances could be and were significantly improved with the application of CEUS. [ABSTRACT FROM AUTHOR]

Objectives: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians’ readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system’s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.Methods: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians’ readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system’s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.Results: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians’ readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system’s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.Conclusion: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians’ readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system’s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.Clinical relevance statement: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians’ readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system’s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.Key Points: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL).This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians’ readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts.The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system’s diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency.The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention.The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS.This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan.94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application. [ABSTRACT FROM AUTHOR]

Objectives: To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion’s surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1–4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92–97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (−1 HU, IQR (−5 to 3.6), vs, 2.5 HU, IQR (−1.5 to 8.5); p < 0.001) whereas we didn’t find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at “standard” 120 kVp.AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.Materials and methods: To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion’s surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1–4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92–97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (−1 HU, IQR (−5 to 3.6), vs, 2.5 HU, IQR (−1.5 to 8.5); p < 0.001) whereas we didn’t find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at “standard” 120 kVp.AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.Results: To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion’s surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1–4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92–97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (−1 HU, IQR (−5 to 3.6), vs, 2.5 HU, IQR (−1.5 to 8.5); p < 0.001) whereas we didn’t find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at “standard” 120 kVp.AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.Conclusion: To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion’s surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1–4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92–97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (−1 HU, IQR (−5 to 3.6), vs, 2.5 HU, IQR (−1.5 to 8.5); p < 0.001) whereas we didn’t find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at “standard” 120 kVp.AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.Clinical relevance statement: To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion’s surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1–4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92–97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (−1 HU, IQR (−5 to 3.6), vs, 2.5 HU, IQR (−1.5 to 8.5); p < 0.001) whereas we didn’t find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at “standard” 120 kVp.AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.Key Points: To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT.IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion’s surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis).We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1–4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92–97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (−1 HU, IQR (−5 to 3.6), vs, 2.5 HU, IQR (−1.5 to 8.5); p < 0.001) whereas we didn’t find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones.AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at “standard” 120 kVp.AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.CT scans are often acquired using peak tube voltage settings different from the “standard” 120 kVp.AA attenuation varies if CT scans are acquired using different tube peak voltage settings.At 100 kVp AAs show a significantly lower attenuation than at 120 kVp. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.The radiomics features were non-reproducible with significant variability in values among different CT techniques.Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.Methods: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.The radiomics features were non-reproducible with significant variability in values among different CT techniques.Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.Results: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.The radiomics features were non-reproducible with significant variability in values among different CT techniques.Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.Conclusion: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.The radiomics features were non-reproducible with significant variability in values among different CT techniques.Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.Clinical relevance statement: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.The radiomics features were non-reproducible with significant variability in values among different CT techniques.Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.Key Points: To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).The test–retest repeatability analysis presented that 97.1% of features were repeatable between scan–rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.The radiomics features were non-reproducible with significant variability in values among different CT techniques.Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models.CT radiomics stability should be guaranteed before the implementation in the clinical routine.Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques.Limited inter-system robustness of radiomic features may impact the generalizability of models. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a deep learning–based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies. Methods: CTA exams of the aorta of 191 patients (68.1 ± 14 years, 128 male), performed between 2015 and 2018, were retrospectively identified from our imaging archive and manually segmented by two investigators. A 3D U-Net model was trained on the data, which was divided into a training, a validation, and a test group at a ratio of 7:1:2. Cases in the test group (n = 41) were evaluated to compare manual and automatic segmentations. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff surface distance (HSD) were extracted. Maximum diameter, effective diameter, and area were quantified and compared between both segmentations at eight anatomical landmarks, and at the maximum area of an aneurysms if present (n = 14). Statistics included error calculation, intraclass correlation coefficient, and Bland-Altman analysis. Results: A DSC of 0.95 [0.94; 0.95] and an MSD of 0.76 [0.06; 0.99] indicated close agreement between segmentations. HSD was 8.00 [4.47; 10.00]. The largest absolute errors were found in the ascending aorta with 0.8 ± 1.5 mm for maximum diameter and at the coeliac trunk with − 30.0 ± 81.6 mm2 for area. Results for absolute errors in aneurysms were − 0.5 ± 2.3 mm for maximum diameter, 0.3 ± 1.6 mm for effective diameter, and 64.9 ± 114.9 mm2 for area. ICC showed excellent agreement (> 0.9; p < 0.05) between quantitative measurements. Conclusions: Automated segmentation of the aorta on CTA data using a deep learning algorithm is feasible and allows for accurate quantification of the aortic lumen even if the vascular architecture is altered by disease. Key Points: • A deep learning–based algorithm can automatically segment the aorta, mostly within acceptable margins of error, even if the vascular architecture is altered by disease. • Quantifications performed in the segmentations were mostly within clinically acceptable limits, even in pathologically altered segments of the aorta. [ABSTRACT FROM AUTHOR]