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Objective: To compare the sensitivity, specificity, and diagnostic accuracy of fat-only datasets reconstructed using a two-point Dixon technique, with corresponding opposed-phase (OP) and in-phase (IP) datasets for characterization of adrenal lesions at 3 Tesla (T).Methods: Fifty-nine patients (21 male, 38 female) with 66 adrenal lesions (49 adenomas, 17 nonadenomas) underwent 3D dual gradient-echo 3-T adrenal MR imaging with reconstruction of OP/IP and fat/water datasets. Sensitivity, specificity, and diagnostic accuracy were compared between OP/IP datasets, using the signal intensity index (SII), and fat/water datasets, using the fat fraction and fat ratio. Four radiologists qualitatively assessed OP/IP and fat-only datasets for reader confidence in lesion characterization and image quality.Results: There were significant differences between adenomas and nonadenomas with regard to mean SII, fat fraction, and fat ratio (P < 0.001). There was no significant difference in mean diagnostic accuracy among different evaluation methods using OP/IP and fat/water datasets. Mean readers' scores for lesion characterization were significantly higher for adenomas than for nonadenomas using OP/IP and fat-only datasets. There was no significant difference between the two datasets regarding mean readers' scores for image quality.Conclusion: Fat-only images can readily differentiate adrenal adenomas from nonadenomas, with diagnostic accuracy comparable to OP/IP images. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To determine whether an unenhanced low-dose image acquired during automated contrast bolus timing can be used to assess hepatic steatosis. Materials and Methods: Fifty subjects (29 male, 21 female; 26-92 years; mean body mass index (BMI; 26.9) with abdominal multiphasic computed tomography were included. Abdominal diameters and circumferences were derived from anteroposterior and lateral scout radiographs. Hepatic attenuation (HA) was measured on unenhanced low-dose images (120 kV; 40 mA; 0.5 seconds' rotation time) and corresponding unenhanced standard-dose images (120 kV, z-axis automatic tube current modulation, noise index 11.5). Noise estimates were measured in surrounding air. Pearson correlation was calculated between abdominal circumference and BMI. Mean HA assessed on low-dose images and standard-dose images was compared using a paired Student's t-test and Bland Altman plots. Results: Abdominal circumference (mean, 142.8cm) correlated well with BMI (r = 0.83). No significant difference was found for HA on low-dose images (mean +57.7 HU) compared to HA on standard-dose images (+56.0 HU) (P = .077). Image noise (+11.5 HU) was significantly higher on low-dose images compared to image noise (+8.1 HU) on standard-dose images (P < .05). For HA mean difference comparing low- and standard-dose images was -1.7 HU (limits of agreement: -14.6, 11.2). Conclusion: In all subjects, hepatic attenuation can be correctly assessed on unenhanced low-dose images. [ABSTRACT FROM AUTHOR]

Streak artifact is an important source of image degradation in computed tomographic imaging. In coronary MDCT angiography, streak artifact from pacemaker leads in the SVC can render segments of the right coronary artery uninterpretable. With current technology in clinical practice, there is no effective way to eliminate streak artifact in coronary MDCT angiography entirely. We propose a technique to minimize the impact of streak artifact in retrospectively gated coronary MDCT angiography by utilizing small shifts in the reconstruction window. In our experience, previously degraded portions of the coronary vasculature were able to be well evaluated using this technique. [ABSTRACT FROM AUTHOR]

Background Report writing skills are a core competency to be acquired during residency, yet objective tools for tracking performance are lacking. Purpose To investigate whether the Jaccard index, derived from report comparison, can objectively illustrate learning curves in report writing performance throughout radiology residency. Materials and Methods Retrospective data from 246 984 radiology reports written from September 2017 to November 2022 in a tertiary care radiology department were included. Reports were scored using the Jaccard similarity coefficient (ie, a quantitative expression of the amount of edits performed; range, 0-1) of residents' draft (unsupervised initial attempt at a complete report) or preliminary reports (following joint readout with attending physicians) and faculty-reviewed final reports. Weighted mean Jaccard similarity was compared between years of experience using Welch analysis of variance with post hoc testing overall, per imaging division, and per modality. Relationships with years and quarters of resident experience were assessed using Spearman correlation. Results This study included 53 residents (mean report count, 4660 ± 3546; 1-5 years of experience). Mean Jaccard similarity of preliminary reports increased by 6% from 1st-year to 5th-year residents (0.86 ± 0.22 to 0.92 ± 0.15; P < .001). Spearman correlation demonstrated a strong relationship between residents' experience and higher report similarity when aggregated for years ( r s = 0.99 [95% CI: 0.85, 1.00]; P < .001) or quarters of experience ( r s = 0.90 [95% CI: 0.73, 0.96]; P < .001). For residents' draft reports, Jaccard similarity increased by 14% over the course of the 5-year residency program (0.68 ± 0.27 to 0.82 ± 0.23; P < .001). Subgroup analysis confirmed similar trends for all imaging divisions and modalities (eg, in musculoskeletal imaging, from 0.77 ± 0.31 to 0.91 ± 0.16 [ P < .001]; r s = 0.98 [95% CI: 0.72, 1.00] [ P < .001]). Conclusion Residents' report writing performance increases with experience. Trends can be quantified with the Jaccard index, with a 6% improvement from 1st- to 5th-year residents, indicating its effectiveness as a tool for evaluating training progress and guiding education over the course of residency. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bruno in this issue.

Background: This study investigates the use of biparametric magnetic resonance imaging (bpMRI) as primary opportunistic screening for prostate cancer (PCa) without using a prostate-specific antigen (PSA) cut-off., Objective: The primary endpoint was to assess the efforts and effectiveness of identifying 20 participants with clinically significant prostate cancer (csPCa) using bpMRI., Design, Setting, and Participants: Biopsy-naïve men aged over 45 yr were included. All participants underwent 3 Tesla bpMRI, PSA, and digital rectal examination (DRE). Targeted-only biopsy was performed in participants with Prostate Imaging Reporting and Data System (PI-RADS) ≥3. Men with negative bpMRI but suspicious DRE or elevated PSA/PSA density had template biopsies. Preintended protocol adjustments were made after an interim analysis for PI-RADS 3 lesions: no biopsy and follow-up MRI after 6 mo and biopsy only if lesions persisted or upgraded., Outcome Measurements and Statistical Analysis: Biopsy results underwent a comparison using Fisher's exact test and univariable logistic regression to identify prognostic factors for positive biopsy., Results and Limitations: A total of 229 men were enrolled in this study, of whom 79 underwent biopsy. Among these men, 77 displayed suspicious PI-RADS lesions. PCa was detected in 29 participants (12.7%), of whom 21 had csPCa (9.2%). Biparametric MRI detected 21 csPCa cases, while PSA and DRE would have missed 38.1%. Protocol adjustment led to a 54.6% biopsy reduction in PI-RADS 3 lesions. Overall, in this cohort of men with a median PSA value of 1.26 ng/ml, 10.9 bpMRI scans were needed to identify one participant with csPCa. A major limitation of the study is the lack of a control cohort undergoing systematic biopsies., Conclusions: Opportunistic screening utilising bpMRI as a primary tool has higher sensitivity in detecting csPCa than classical screening methods., Patient Summary: Screening with biparametric magnetic resonance imaging (bpMRI) and targeted biopsy identified clinically significant prostate cancer in every 11th man, regardless of the prostate-specific antigen (PSA) levels. Preselecting patients based on PSA >1 ng/ml and a positive family history of prostate cancer, as well as other potential blood tests may further improve the effectiveness of bpMRI in this setting., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: To investigate how a transition from free text to structured reporting affects reporting language with regard to standardization and distinguishability., Methods: A total of 747,393 radiology reports dictated between January 2011 and June 2020 were retrospectively analyzed. The body and cardiothoracic imaging divisions introduced a reporting concept using standardized language and structured reporting templates in January 2016. Reports were segmented by a natural language processing algorithm and converted into a 20-dimension document vector. For analysis, dimensionality was reduced to a 2D visualization with t-distributed stochastic neighbor embedding and matched with metadata. Linguistic standardization was assessed by comparing distinct report types' vector spreads (e.g., run-off MR angiography) between reporting standards. Changes in report type distinguishability (e.g., CT abdomen/pelvis vs. MR abdomen) were measured by comparing the distance between their centroids., Results: Structured reports showed lower document vector spread (thus higher linguistic similarity) compared with free-text reports overall (21.9 [free-text] vs. 15.9 [structured]; - 27.4%; p < 0.001) and for most report types, e.g., run-off MR angiography (15.2 vs. 1.8; - 88.2%; p < 0.001) or double-rule-out CT (26.8 vs. 10.0; - 62.7%; p < 0.001). No changes were observed for reports continued to be written in free text, e.g., CT head reports (33.2 vs. 33.1; - 0.3%; p = 1). Distances between the report types' centroids increased with structured reporting (thus better linguistic distinguishability) overall (27.3 vs. 54.4; + 99.3 ± 98.4%) and for specific report types, e.g., CT abdomen/pelvis vs. MR abdomen (13.7 vs. 37.2; + 171.5%)., Conclusion: Structured reporting and the use of factual language yield more homogenous and standardized radiology reports on a linguistic level, tailored to specific reporting scenarios and imaging studies., Clinical Relevance: Information transmission to referring physicians, as well as automated report assessment and content extraction in big data analyses, may benefit from standardized reporting, due to consistent report organization and terminology used for pathologies and normal findings., Key Points: • Natural language processing and t-distributed stochastic neighbor embedding can transform radiology reports into numeric vectors, allowing the quantification of their linguistic standardization. • Structured reporting substantially increases reports' linguistic standardization (mean: - 27.4% in vector spread) and distinguishability (mean: + 99.3 ± 98.4% increase in vector distance) compared with free-text reports. • Higher standardization and homogeneity outline potential benefits of structured reporting for information transmission and big data analyses., (© 2023. The Author(s).)

Purpose: To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images., Materials and Methods: In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients to evaluate the model's performance. The trained algorithm was applied to a second dataset of 4004 whole-body CT examinations to investigate age-dependent volume and attenuation changes., Results: The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major abnormalities. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 vs 0.871; P < .001). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (eg, age and aortic volume [ r s = 0.64; P < .001]; age and mean attenuation of the autochthonous dorsal musculature [ r s = -0.74; P < .001])., Conclusion: The developed model enables robust and accurate segmentation of 104 anatomic structures. The annotated dataset ( https://doi.org/10.5281/zenodo.6802613 ) and toolkit ( https://www.github.com/wasserth/TotalSegmentator ) are publicly available. Keywords: CT, Segmentation, Neural Networks Supplemental material is available for this article . © RSNA, 2023See also commentary by Sebro and Mongan in this issue., Competing Interests: Disclosures of conflicts of interest: J.W. No relevant relationships. H.C.B. No relevant relationships. M.T.M. No relevant relationships. M.P. No relevant relationships. D.H. No relevant relationships. A.W.S. No relevant relationships. T.H. No relevant relationships. D.T.B. No relevant relationships. J.C. No relevant relationships. S.Y. No relevant relationships. M.B. No relevant relationships. M.S. No relevant relationships., (© 2023 by the Radiological Society of North America, Inc.)

Background: Diffuse parenchymal liver diseases are contributing substantially to global morbidity and represent major causes of deaths worldwide. The aim of our study is to assess whether established hepatic fat and iron quantitation and relaxometry-based quantification of hepatocyte-specific contrast material as surrogate for liver function estimation allows to evaluate liver fibrosis., Methods: Retrospective consecutive study. Seventy-two healthy patients (mean age: 53 years) without known liver disease, 21 patients with temporary elevated liver enzymes (mean: 65 years) and 109 patients with biopsy proven liver fibrosis or cirrhosis (mean: 61 years), who underwent liver magnetic resonance imaging (MRI) with a hepatocyte-specific contrast agent [gadoxetate disodium, gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA), 0.25 mmol/mL Primovist, Bayer AG, Leverkusen, Germany] at 1.5 T (n=133) and at 3 T (n=69), were included. Fibrosis was classified using the histopathological meta-analysis of histological data in viral hepatitis (METAVIR) and the clinical Child-Pugh scores. Gd-concentration were quantified using T1 map-based calculations. Gd-concentration mapping was performed by using a Look-Locker approach prior to and 912±159 s after intravenous administration of hepatocyte specific contrast agent. Additionally, parenchymal fat fraction, R2*, bilirubin, gender and age were defined as predicting factors. Diagnostic accuracy was calculated in a monoparametric (linear regression, predictor: Gd-concentration) and multiparametric model (predictors: age, bilirubin level, iron overload, liver fat fraction, Gd concentration in the left and right liver lobe)., Results: Mean Gd-concentration in the liver parenchyma was significantly higher for healthy patients ([Gd] =0.51 µmol/L) than for those with liver fibrosis or cirrhosis ([Gd] =0.31 µmol/L; P<0.0001) and with acute liver disease ([Gd] =0.28 µmol/L), though there were no significant differences for the latter two groups. There was a significant moderate negative correlation for the mean Gd-concentration and the METAVIR score (ρ=-0.44, P<0.0001) as well as for the Child-Pugh stage (ρ=-0.35, P<0.0001). There was a significant strong correlation between the bilirubin concentration and the Gd-concentration (ρ=-0.61, P<0.0001). The diagnostic accuracy for the discrimination of healthy patients and patients with known fibrosis or cirrhosis was 0.74 (0.71/0.60 sensitivity/specificity) in a monoparametric and 0.76 (0.85/0.61 sensitivity/specificity) in a machine learning based multiparametric model., Conclusions: T1 mapping-based quantification of hepatic Gd-EOB-DTPA concentrations performed in a multiparametric model shows promising diagnostic accuracy for the detection of fibrotic changes. Liver biopsy might be replaced by imaging examinations., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-884/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Hypertensive heart disease (HHD) develops in response to the chronic exposure of the left ventricle and left atrium to elevated systemic blood pressure. Left ventricular structural changes include hypertrophy and interstitial fibrosis that in turn lead to functional changes including diastolic dysfunction and impaired left atrial and LV mechanical function. Ultimately, these changes can lead to heart failure with a preserved (HFpEF) or reduced (HFrEF) ejection fraction. This review will outline the clinical evaluation of a patient with hypertension and/or suspected HHD, with a particular emphasis on the role and recent advances of multimodality imaging in both diagnosis and differential diagnosis.

Purpose: To assess whether high temporal/spatial resolution GRASP MRI acquired during routine clinical imaging can identify several degrees of renal function impairment referenced against renal dynamic scintigraphy., Methods: This retrospective study consists of method development and method verification parts. During method development, patients subject to renal imaging using gadoterate meglumine and GRASP post-contrast MRI technique (TR/TE 3.3/1.6 ms; FoV320 × 320 mm; FA12°; Voxel1.1 × 1.1x2.5 mm) were matched into four equally-sized renal function groups (no-mild-moderate-severe impairment) according to their laboratory-determined estimated glomerular filtration rates (eGFR); 60|120 patients|kidneys were included. Regions-of-interest (ROIs) were placed on cortices, medullary pyramids and collecting systems of bilateral kidneys. Cortical perfusion, tubular concentration and collecting system excretion were determined as Time Cortex=Pyramid (sec), Slope Tubuli (sec -1 ), and Time Collecting System (sec), respectively, and were measured by a combination of extraction of time intensity curves and respective quantitative parameters. For method verification, patients subject to GRASP MRI and renal dynamic scintigraphy (99mTc-MAG3, 100 MBq/patient) were matched into three renal function groups (no-mild/moderate-severe impairment). Split renal function parameters post 1.5-2.5 min as well as MAG3 TER were correlated with time intensity parameters retrieved using GRASP technique; 15|30 patients|kidneys were included., Results: Method development showed differing values for Time Cortex=Pyramid (71|75|93|122 s), Slope Tubuli (2.6|2.1|1.3|0.5 s -1 ) and Time Collecting System (90|111|129|139 s) for the four renal function groups with partial significant tendencies (several p-values <  0.001). In method verification, 29/30 kidneys (96.7%) were assigned to the correct renal function group., Conclusion: High temporal and spatial resolution GRASP MR imaging allows to identify several degrees of renal function impairment using routine clinical imaging with a high degree of accuracy., (© 2023. The Author(s).)

Purpose: To assess image quality and metal artifact reduction in split-filter dual-energy CT (sfDECT) of the abdomen with hip or spinal implants using virtual monoenergetic images (VMI) and iterative metal artifact reduction algorithm (iMAR)., Methods: 102 portal-venous abdominal sfDECTs of patients with hip (n = 71) or spinal implants (n = 31) were included in this study. Images were reconstructed as 120kVp-equivalent images (Mixed) and VMI (40-190 keV), with and without iMAR. Quantitative artifact and image noise was measured using 12 different ROIs. Subjective image quality was rated by two readers using a five-point Likert-scale in six categories, including overall image quality and vascular contrast., Results: Lowest quantitative artifact in both hip and spinal implants was measured in VMI 190keV-iMAR . However, it was not significantly lower than in Mixed iMAR (for all ROIs, p = 1.00), which were rated best for overall image quality (hip: 1.00 [IQR: 1.00-2.00], spine: 3.00 [IQR:2.00-3.00]). VMI 50keV-iMAR was rated best for vascular contrast (hip: 1.00 [IQR: 1.00-2.00], spine: 2.00 [IQR: 1.00-2.00]), which was significantly better than Mixed (both, p < 0.001). VMI 50keV-iMAR provided superior overall image quality compared to Mixed for hip (1.00 vs 2.00, p < 0.001) and similar diagnostic image quality for spinal implants (2.00 vs 2.00, p = 0.51)., Conclusion: For abdominal sfDECT with hip or spinal implants Mixed iMAR images should be used. High keV VMI do not further improve image quality. IMAR allows the use of low keV images (VMI 50keV ) to improve vascular contrast, compared to Mixed images., (© 2022. The Author(s).)

Acute bowel ischemia is a condition with high mortality and requires rapid intervention to avoid catastrophic outcomes. Swift and accurate imaging diagnosis is essential because clinical findings are commonly nonspecific. Conventional contrast enhanced CT of the abdomen has been the imaging modality of choice to evaluate suspected acute bowel ischemia. However, subtlety of image findings and lack of non-contrast or arterial phase images can make correct diagnosis challenging. Dual-energy CT provides valuable information toward assessing bowel ischemia. Dual-energy CT exploits the differential X-ray attenuation at two different photon energy levels to characterize the composition of tissues and reveal the presence or absence of faint intravenous iodinated contrast to improve reader confidence in detecting subtle bowel wall enhancement. With the same underlying technique, virtual non-contrast images can help to show non-enhancing hyperdense hemorrhage of the bowel wall in intravenous contrast-enhanced scans without the need to acquire actual non-contrast scans. Dual-energy CT derived low photon energy (keV) virtual monoenergetic images emphasize iodine contrast and provide CT angiography-like images from portal venous phase scans to better evaluate abdominal arterial patency. In Summary, dual-energy CT aids diagnosing acute bowel ischemia in multiple ways, including improving visualization of the bowel wall and mesenteric vasculature, revealing intramural hemorrhage in contrast enhanced scans, or possibly reducing intravenous contrast dose., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: VPM1002BC is a genetically modified Mycobacterium bovis bacillus Calmette-Guérin (BCG) strain with potentially improved immunogenicity and attenuation., Objective: To report on the efficacy, safety, tolerability and quality of life of intravesical VPM1002BC for the treatment of non-muscle-invasive bladder cancer (NMIBC) recurrence after conventional BCG therapy., Design, Setting, and Participants: We designed a phase 1/2 single-arm trial (NCT02371447). Patients with recurrent NMIBC after BCG induction ± BCG maintenance therapy and intermediate to high risk for cancer progression were eligible., Intervention: Patients were scheduled for standard treatment of six weekly instillations with VPM1002BC followed by maintenance for 1 yr. Treatment was stopped in cases of recurrence., Outcome Measurements and Statistical Analysis: The primary endpoint was defined as the recurrence-free rate (RFR) in the bladder 60 wk after trial registration. The sample size was calculated based on the assumption that ≥30% of the patients would be without recurrence at 60 wk after registration., Results and Limitations: After exclusion of two ineligible patients, 40 patients remained in the full analysis set. All treated tumours were of high grade and 27 patients (67.5%) presented with carcinoma in situ. The recurrence-free rate in the bladder at 60 wk after trial registration was 49.3% (95% confidence interval [CI] 32.1-64.4%) and remained at 47.4% (95% CI 30.4-62.6%] at 2 yr and 43.7% (95% CI 26.9-59.4%) at 3 yr after trial registration. At the same time, progression to muscle-invasive disease had occurred in three patients and metastatic disease in four patients. Treatment-related grade 1, 2, and 3 adverse events (AEs) were observed in 14.3%, 54.8%, and 4.8% of the patients, respectively. No grade ≥4 AEs occurred. Two of the 42 patients did not tolerate five or more instillations during induction. Limitations include the single-arm trial design and the low number of patients for subgroup analysis., Conclusions: At 1 yr after treatment start, almost half of the patients remained recurrence-free after therapy with VPM100BC. The primary endpoint of the study was met and the therapy is safe and well tolerated., Patient Summary: We conducted a trial of VPM100BC, a genetically modified bacillus Calmette-Guérin (BCG) strain for treatment of bladder cancer not invading the bladder muscle. At 1 year after the start of treatment, almost half of the patients with a recurrence after previous conventional BCG were free from non-muscle-invasive bladder cancer (NMIBC). The results are encouraging and VPM1002BC merits further evaluation in randomised studies for patients with NMIBC., (Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background: Liver steatosis is common and tracking disease evolution to steatohepatitis and cirrhosis is essential for risk stratification and resultant patient management. Consequently, diagnostic tools allowing categorization of liver parenchyma based on routine imaging are desirable. The study objective was to compare established mono-factorial, dynamic single parameter and iterative multiparametric routine computed tomography (CT) and magnetic resonance imaging (MRI) analyses to distinguish between liver steatosis, steatohepatitis, cirrhosis and normal liver parenchyma., Methods: A total of 285 multi-phase contrast enhanced CT and 122 MRI studies with histopathological correlation of underlying parenchymal condition were retrospectively included. Parenchymal conditions were characterized based on CT Hounsfield units (HU) or MRI signal intensity (SI) measurements and calculated HU or SI ratios between non-contrast and contrast enhanced imaging time points. First, the diagnostic accuracy of mono-factorial analyses using established, static non-contrast HU and in- to opposed phase SI change cut-offs to distinguish between parenchymal conditions was established. Second, single dynamic discriminator analyses, with optimized non-contrast and enhancement HU and SI ratio cut-off values derived from the data, employing receiver operating characteristic (ROC) curve areas under the curve (AUCs) and the Youden index for maximum accuracy, were used for disease diagnosis. Third, multifactorial analyses, employing multiple non-contrast and contrast enhanced HU and SI ratio cut-offs in a nested, predictive-modelling algorithm were performed to distinguish between normal parenchyma, liver steatosis, steatohepatitis and cirrhosis. CT and MRI analyses were performed separately., Results: No single CT or MRI parameter showed significant difference between all four parenchymal conditions (each P>0.05). Mono-factorial static-CT-discriminator analyses identified liver steatosis with 75% accuracy. Mono-factorial MRI analyses identified steatosis with 89% accuracy. Single-dynamic CT parameter analyses identified normal parenchyma with 72% accuracy and cirrhosis with 75% accuracy. Single-dynamic MRI parameter analyses identified fatty parenchyma with 90% accuracy. Multifactorial CT analyzes identified normal parenchyma with 84%, liver steatosis with 95%, steatohepatitis with 95% and cirrhosis with 80% accuracy. Multifactorial predictive modelling of MRI parameters identified normal parenchyma with 79%, liver steatosis with 89%, steatohepatitis with 92% and cirrhosis with 89% accuracy., Conclusions: Multiparametric analyses of quantitative measurements derived from routine CT and MRI, utilizing a predictive modelling algorithm, can help to distinguish between normal liver parenchyma, liver steatosis, steatohepatitis and cirrhosis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-384). The authors have no conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objectives: The goal of this study was to investigate the precise timeline of respiratory events occurring after the administration of two gadolinium-based contrast agents, gadoxetate disodium and gadoterate meglumine., Materials and Methods: This retrospective study examined 497 patients subject to hepatobiliary imaging using the GRASP MRI technique (TR/TE = 4/2 ms; ST = 2.5 mm; 384 × 384 mm). Imaging was performed after administration of gadoxetate (N = 338) and gadoterate (N = 159). All GRASP datasets were reconstructed using a temporal resolution of 1 s. Four regions-of-interest (ROIs) were placed in the liver dome, the right and left cardiac ventricle, and abdominal aorta detecting liver displacement and increasing vascular signal intensities over time. Changes in hepatic intensity reflected respiratory dynamics in temporal correlation to the vascular contrast bolus., Results: In total, 216 (67%) and 41 (28%) patients presented with transient respiratory motion after administration of gadoxetate and gadoterate, respectively. The mean duration from start to acme of the respiratory episode was similar (p = 0.4) between gadoxetate (6.0 s) and gadoterate (5.6 s). Its mean onset in reference to contrast arrival in the right ventricle differed significantly (p < 0.001) between gadoxetate (15.3s) and gadoterate (1.8 s), analogously to peak inspiration timepoint in reference to the aortic enhancement arrival (gadoxetate: 0.9s after, gadoterate: 11.2 s before aortic enhancement, p < 0.001)., Conclusions: The timepoint of occurrence of transient respiratory anomalies associated with gadoxetate disodium and gadoterate meglumine differs significantly between both contrast agents while the duration of the event remains similar., Key Points: • Transient respiratory anomalies following the administration of gadoterate meglumine occurred during a time period usually not acquired in MR imaging. • Transient respiratory anomalies following the administration of gadoxetate disodium occurred around the initiation of arterial phase imaging. • The estimated duration of respiratory events was similar between both contrast agents., (© 2021. The Author(s).)

Although transarterial chemoembolization (TACE) is the most widely used treatment for intermediate-stage, unresectable hepatocellular carcinoma (HCC), it is only effective in a subset of patients. In this study, we combine clinical, radiological, and genomics data in supervised machine-learning models toward the development of a clinically applicable predictive classifier of response to TACE in HCC patients. Our study consists of a discovery cohort of 33 tumors through which we identify predictive biomarkers, which are confirmed in a validation cohort. We find that radiological assessment of tumor area and several transcriptomic signatures, primarily the expression of FAM111B and HPRT1, are most predictive of response to TACE. Logistic regression decision support models consisting of tumor area and RNA-seq gene expression estimates for FAM111B and HPRT1 yield a predictive accuracy of ∼90%. Reverse transcription droplet digital PCR (RT-ddPCR) confirms these genes in combination with tumor area as a predictive classifier for response to TACE., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

Objective: The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans., Materials and Methods: We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated., Results: The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001)., Conclusions: Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time., Competing Interests: Conflicts of interest and sources of funding: D.J.W. receives research support from the Swiss Society of Radiology and the Research Fund Junior Researchers of the University Hospital Basel (grant no. 3MS1034)., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Methods: A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation., Results: There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%)., Conclusions: Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions., Competing Interests: Conflicts of interest and sources of funding: none declare., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: To evaluate potential confounding factors in the quantitative assessment of liver fibrosis and cirrhosis using T1 relaxation times., Methods: The study population is based on a radiology-information-system database search for abdominal MRI performed from July 2018 to April 2019 at our institution. After applying exclusion criteria 200 (59 ± 16 yrs) remaining patients were retrospectively included. 93 patients were defined as liver-healthy, 40 patients without known fibrosis or cirrhosis, and 67 subjects had a clinically or biopsy-proven liver fibrosis or cirrhosis. T1 mapping was performed using a slice based look-locker approach. A ROI based analysis of the left and the right liver was performed. Fat fraction, R2*, liver volume, laboratory parameters, sex, and age were evaluated as potential confounding factors., Results: T1 values were significantly lower in healthy subjects without known fibrotic changes (1.5 T MRI: 575 ± 56 ms; 3 T MRI: 857 ± 128 ms) compared to patients with acute liver disease (1.5 T MRI: 657 ± 73 ms, p < 0.0001; 3 T MRI: 952 ± 37 ms, p = 0.028) or known fibrosis or cirrhosis (1.5 T MRI: 644 ± 83 ms, p < 0.0001; 3 T MRI: 995 ± 150 ms, p = 0.018). T1 values correlated moderately with the Child-Pugh stage at 1.5 T (p = 0.01, ρ = 0.35)., Conclusion: T1 mapping is a capable predictor for detection of liver fibrosis and cirrhosis. Especially age is not a confounding factor and, hence, age-independent thresholds can be defined. Acute liver diseases are confounding factors and should be ruled out before employing T1-relaxometry based thresholds to screen for patients with liver fibrosis or cirrhosis., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: Pancreatic islet-cell tumors (PICT) often present with atypical signal-characteristics and are often missed on preoperative imaging. The aim of this study is to provide a multiparametric PICT characterization and investigate factors impeding PICT detection., Material and Methods: This is a detailed MRI analysis of a prospective, monocenter study, including 49 consecutive patients (37 female, 12 male; median age 50) with symptoms due to endogenous hyperinsulinemic hypoglycemia (EHH) and mostly negative prior-imaging. All patients received a 3-T MRI and a 68Ga-DOTA-exendin-4-PET/CT. Pooled accuracy, sensitivity, specificity and inter-reader agreement were calculated. Reference-standard was histopathology and 68Ga-DOTA-Exendin-4-PET/CT in one patient who refused surgery. For PICT analyses, 34 patients with 49 PICTs (48 histologically proven; one 68Ga-DOTA-exendin-4-PET/CT positive) were assessed. Dynamic contrast-enhanced (DCE) Magnetic Resonance Images (MRI) with Golden-Angle-Radial-Sparse-Parallel (GRASP) reconstruction, enabling imaging at high spatial and temporal resolution, was used to assess enhancement-patterns of PICTs. Tumor-to-background (T2B) ratio for each sequence and the employed quantitative threshold for conspicuity of PICTs were analyzed in regard to prediction of true-positive PICTs., Results: Evaluation of 49 patients revealed a pooled lesion-based accuracy, sensitivity and specificity of 70.3%, 72.9% and 62.5%, respectively. Mean PICT size was 12.9±5.3mm for detected, 9.0±2.9mm for undetected PICTs (p-value 0.0112). In-phase T1w detected the most PICT (67.3%). Depending on the sequence, PICTs were isointense and poorly visible in 29-68%. Only 2/41(4.9%) PICTs showed typical signal-characteristics across T1w, T2w, DWI and ceT1w combined. 66.6% of PICTs enhanced simultaneously to the parenchyma, 17.8% early and 15.6% late. Predictor screening analysis showed number of sequences detecting a PICT, lesion size and in-phase T1w T2B ratio had the highest contribution for detecting a true-positive PICT., Conclusion: The majority of PICTs enhance simultaneously to surrounding parenchyma, present with atypical signal-characteristics and thus are poorly visible. In non-enhancing PICTs, radiologists should search for small lesions most likely conspicuous on unenhanced T1w or DWI., Competing Interests: The authors have declared that no competing interests exist.

Objectives: To investigate if nested multiparametric decision tree models based on tumor size and CT texture parameters from pre-therapeutic imaging can accurately predict hepatocellular carcinoma (HCC) lesion response to transcatheter arterial chemoembolization (TACE)., Materials and Methods: This retrospective study (January 2011-September 2017) included consecutive pre- and post-therapeutic dynamic CT scans of 37 patients with 92 biopsy-proven HCC lesions treated with drug-eluting bead TACE. Following manual segmentation of lesions according to modified Response Evaluation Criteria in Solid Tumors criteria on baseline arterial phase CT images, tumor size and quantitative texture parameters were extracted. HCCs were grouped into lesions undergoing primary TACE (VT-lesions) or repeated TACE (RT-lesions). Distinct multiparametric decision tree models to predict complete response (CR) and progressive disease (PD) for the two groups were generated. AUC and model accuracy were assessed., Results: Thirty-eight of 72 VT-lesions (52.8%) and 8 of 20 RT-lesions (40%) achieved CR. Sixteen VT-lesions (22.2%) and 8 RT-lesions (40%) showed PD on follow-up imaging despite TACE treatment. Mean of positive pixels (MPP) was significantly higher in VT-lesions compared to RT-lesions (180.5 vs 92.8, p = 0.001). The highest AUC in ROC curve analysis and accuracy was observed for the prediction of CR in VT-lesions (AUC 0.96, positive predictive value 96.9%, accuracy 88.9%). Prediction of PD in VT-lesions (AUC 0.88, accuracy 80.6%), CR in RT-lesions (AUC 0.83, accuracy 75.0%), and PD in RT-lesions (AUC 0.86, accuracy 80.0%) was slightly inferior., Conclusions: Nested multiparametric decision tree models based on tumor heterogeneity and size can predict HCC lesion response to TACE treatment with high accuracy. They may be used as an additional criterion in the multidisciplinary treatment decision-making process., Key Points: • HCC lesion response to TACE treatment can be predicted with high accuracy based on baseline tumor heterogeneity and size. • Complete response of HCC lesions undergoing primary TACE was correctly predicted with 88.9% accuracy and a positive predictive value of 96.9%. • Progressive disease was correctly predicted with 80.6% accuracy for lesions undergoing primary TACE and 80.0% accuracy for lesions undergoing repeated TACE.

Pancreatic cystic lesions (PCL) are a frequent and underreported incidental finding on CT scans and can transform into neoplasms with devastating consequences. We developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of PCL on CTs. A total of 543 cysts on 221 abdominal CTs were manually segmented in 3D by a radiology resident in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a two-step nnU-Net for detection with the performance assessed depending on lesions' volume and location in comparison to three human readers of varying experience. Mean sensitivity was 78.8 ± 0.1%. The sensitivity was highest for large lesions with 87.8% for cysts ≥220 mm 3 and for lesions in the distal pancreas with up to 96.2%. The number of false-positive detections for cysts ≥220 mm 3 was 0.1 per case. The algorithm's performance was comparable to human readers. To conclude, automated detection of PCL on CTs is feasible. The proposed model could serve radiologists as a second reading tool. All imaging data and code used in this study are freely available online.

Objectives: To investigate the most common errors in residents' preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety., Material and Methods: Changes in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups., Results: Overall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072-0.117 per report; once every 9-14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged., Conclusion: Residents' most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required., Key Points: • The two most common reporting errors in residents' preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties.

Background Workloads in radiology departments have constantly increased over the past decades. The resulting radiologist fatigue is considered a rising problem that affects diagnostic accuracy. Purpose To investigate whether data mining of quantitative parameters from the report proofreading process can reveal daytime and shift-dependent trends in report similarity as a surrogate marker for resident fatigue. Materials and Methods Data from 117 402 radiology reports written by residents between September 2017 and March 2020 were extracted from a report comparison tool and retrospectively analyzed. Through calculation of the Jaccard similarity coefficient between residents' preliminary and staff-reviewed final reports, the amount of edits performed by staff radiologists during proofreading was quantified on a scale of 0 to 1 (1: perfect similarity, no edits). Following aggregation per weekday and shift, data were statistically analyzed by using simple linear regression or one-way analysis of variance (significance level, P < .05) to determine relationships between report similarity and time of day and/or weekday reports were dictated. Results Decreasing report similarity with increasing work hours was observed for day shifts ( r = -0.93 [95% CI: -0.73, -0.98]; P < .001) and weekend shifts ( r = -0.72 [95% CI: -0.31, -0.91]; P = .004). For day shifts, negative linear correlation was strongest on Fridays ( r = -0.95 [95% CI: -0.80, -0.99]; P < .001), with a 16% lower mean report similarity at the end of shifts (0.85 ± 0.24 at 8 am vs 0.69 ± 0.32 at 5 pm). Furthermore, mean similarity of reports dictated on Fridays (0.79 ± 0.35) was lower than that on all other weekdays (range, 0.84 ± 0.30 to 0.86 ± 0.27; P < .001). For late shifts, report similarity showed a negative correlation with the course of workweeks, showing a continuous decrease from Monday to Friday ( r = -0.98 [95% CI: -0.70, -0.99]; P = .007). Temporary increases in report similarity were observed after lunch breaks (day and weekend shifts) and with the arrival of a rested resident during overlapping on-call shifts. Conclusion Decreases in report similarity over the course of workdays and workweeks suggest aggravating effects of fatigue on residents' report writing performances. Periodic breaks within shifts potentially foster recovery. © RSNA, 2021.

Background: Tuberous Sclerosis Complex (TSC) is a genetic disorder, with renal manifestations like angiomyolipoma (AML) occurring in 70-80% of patients. AML usually cause more complications in TCS patients than in non-TSC patients. However, AML patients are not routinely investigated for TSC. Our aim was to retrospectively assess the correlation between radiologically diagnosed AML and TSC., Methods: All patients were stratified into AML related vs. unrelated to TSC. Correlations were calculated to determine the association between age, AML, and TSC., Results: Complete data were available for 521 patients with renal AML, in 7 of which the concurrent diagnosis of TSC was found. Younger age significantly positively correlated with the prevalence of TSC in AML patients (p <  0.01). 37 (7%) of the 521 patients were within the age-range of 18-40 years, in which TSC occurred in 6 cases, 4 (66.7%) of which presented with multiple, bilateral renal AML (p <  0.05), and 2 (33.3%) of which with a single, unilateral AML (p <  0.05). In patients with AML but without TSC, unilateral AML was found in 83.9% and bilateral AML in 16.1% (p <  0.05). Simple binary logistic regression analysis revealed bilateral AML (OR 33.0; 95% CI 3.2-344.0; p = 0.003) (but not unilateral AML (OR 0.09; 95% CI 0.01-0.88; p = 0.04)) to be a risk factor for TSC., Conclusions: The presence of bilateral AML in patients within the age-range of 18-40 years should raise suspicion for TSC as the underlying cause. Therefore, our advice is to refer patients with multiple bilateral renal AML for further investigations regarding TSC.

Background: Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports-serving as the ground truth-was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI., Conclusions: The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.

Objective: To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) classifier could better classify prostate cancer (PCa) risk groups than T2w/DWI sequences alone., Materials and Methods: One hundred ninety patients (68 ± 9 years) were retrospectively evaluated at 3T MRI for clinical suspicion of PCa. Included were 201 peripheral zone (PZ) PCa lesions. Histopathological confirmation on fusion biopsy was matched with normal prostate parenchyma contralaterally. Biopsy results were grouped into benign tissue and low-, intermediate-, and high-risk groups (Gleason sum score 6, 7, and > 7, respectively). DCE MRI was performed using golden-angle radial sparse MRI. Perfusion maps (K trans , K ep , V e ), apparent diffusion coefficient (ADC), and absolute T2w signal intensity were determined and used as input features for building two ML models: GBM with/without perfusion maps. Areas under the receiver operating characteristic curve (AUC) values for correlated models were compared., Results: For the classification of benign vs. malignant and intermediate- vs. high-grade PCa, perfusion information added relevant information (AUC values 1 vs. 0.953 and 0.909 vs. 0.700, p < 0.001 and p = 0.038), while no statistically significant effect was found for low- vs. intermediate- and high-grade PCa., Conclusion: Perfusion information from DCE MRI acquisition schemes with high spatiotemporal resolution to ML classifiers enables a superior risk stratification between benign and malignant and intermediate- and high-risk PCa in the PZ compared with classifiers based on T2w/DWI information alone., Key Points: • In the recent guidelines, the role of DCE MRI has changed from a mandatory to recommended sequence. • DCE MRI acquisition schemes with high spatiotemporal resolution (e.g., GRASP) have been shown to improve the diagnostic performance compared with conventional DCE MRI sequences. • Using perfusion information acquired with GRASP in combination with ML classifiers significantly improved the prediction of benign vs. malignant and intermediate- vs. high-grade peripheral zone prostate cancer compared with non-contrast sequences.

Microsurgical abdominally-based reconstruction is considered the gold standard in autologous breast reconstruction. Despite refined surgical procedures, donor-site complications still occur, reducing patient satisfaction and quality of life. Recent work has outlined the potential of morphometric measurements in risk assessment for postoperative hernia development. With rising demand for personalised treatment, the goal of this study was to investigate their potential in risk assessment for any donor site complication. In this retrospective cohort study, 90 patients were included who each received microsurgical breast reconstruction at the hands of one surgeon between January 2015 and May 2017. Donor-site complications formed the primary outcome and were classified according to Clavien-Dindo. Morphometric measurements were taken on a routinely performed computed tomographic angiogram. Complications occurred in 13 of the 90 (14.4%) cases studied. All patients who developed any type of postoperative donor site complication had a history of abdominal surgery. The risk of postoperative complications increased by 3% with every square centimetre of omental fat tissue (OR 1.03, 95% CI 1.00-1.06, and p -value = 0.022). Morphometric measurements provide valuable information in risk assessment for donor-site complications in abdominally-based breast reconstruction. They may help identify personalised reconstructive options for maximal postoperative patient satisfaction and quality of life.

Purpose: The aim of this study was to investigate whether structured reports (SRs) of prostate MRI results are more suitable than non-structured reports (NSRs) for promoting the more accurate assessment of the location of a single prostate cancer lesion by novices in MRI-targeted biopsy., Methods: 50 NSRs and 50 SRs describing a single prostatic lesion were presented to 5 novices in MRI-targeted biopsy. The participants were asked to plot the tumor location in a two-dimensional prostate diagram and to answer a questionnaire on the quality of the reports. The accuracy of the plotted tumor position was evaluated with a validated 30-point scoring system that distinguished between "major" and "minor" mistakes., Results: The overall mean score for the accuracy of the tumor plotting was significantly higher for SRs than for NSRs (26.4 vs. 20.7, p < 0.01). The mean numbers of major (1.4 vs. 0.48, p < 0.01) and minor (3.05 vs. 1.15, p < 0.01) mistakes were significantly higher for NSRs than for SRs. Compared with NSRs, SRs received significantly higher ratings for the perceived quality of the summary (4.0 vs. 2.4, p < 0.01) as well as for the overall satisfaction with the report (4.1 vs. 2.1, p < 0.01)., Conclusion: Novices in MRI-targeted biopsy prefer structured reporting of prostate MRI as an information tool. SRs allow for a more accurate assessment of the location of single prostate cancer lesions. Therefore, structured reporting of prostate MRI may help to foster the learning process of novices in MRI-targeted biopsy.

Purpose: To establish simple quantitative variables at short-tau inversion recovery (STIR) magnetic resonance imaging (MRI) to identify lipomas with high specificity in patients with indeterminate subfascial lipomatous tumors., Materials and Methods: The MRI examinations of 26 patients (14 men, 12 women; mean age 63±12.5 [SD] years; range: 40-84years) with histopathologically proven subfascial atypical lipomatous tumors/well-differentiated liposarcomas (ALT/WDLs) and those of 68 patients (32 men, 36 women; mean age, 56±13.5 [SD] years; range: 21-83years) with lipomas were retrospectively reviewed. Ratios derived from region of interest based signal intensity (SI) measurements of tumors and adjacent fat on STIR images were calculated and maximum tumor diameters were noted. Diagnostic parameter capabilities were assessed using ROC curve analysis. Interreader agreement was evaluated by calculation of intraclass correlation coefficients (ICC)., Results: Using a cut-off value of 1.18, STIR-SI ratios allowed discriminating between lipoma and ALT/WDL (AUC=0.88; P<0.001) yielding 93% specificity (95% CI: 77-99%) and 74% sensitivity (95% CI: 61-84%) for the diagnosis of lipoma. Interreader agreement was excellent (ICC=0.93). A significant difference in maximum tumor diameter was found between ALT/WDLs (mean: 18.1±6.0 [SD] cm; range: 5.6-33.1cm) and lipomas (mean: 9.7±5.0 [SD] cm; range: 2.9-29.1cm) (P<0.001). Using a cut-off of 11cm, maximum tumor diameter allowed discriminating between lipoma and ALT/WDLs with 92% specificity (95% CI: 75-99%) and 69% sensitivity (95% CI: 57-80%). The combination of a STIR-SI ratio<1.4 and maximum tumor diameter<11cm yielded 100% specificity (95% CI: 87-100%) and 65% sensitivity (95% CI: 54-77%) for the diagnosis of lipoma., Conclusion: The combination of STIR-SI ratio and maximum diameter allows discriminating between lipoma and ALT/WDL in initially indeterminate lipomatous tumors., (Copyright © 2020 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)

Purpose: To establish thresholds for contrast enhancement-based attenuation (CM) and iodine concentration (IOD) for the quantitative evaluation of enhancement in renal lesions on single-phase split-filter dual-energy CT (tbDECT) and combine measurements in a machine learning algorithm to potentially improve performance., Material: 126 patients with incidental renal cysts (both hypo- and hyperdense cysts) or high suspicion for renal cell carcinoma (312 total lesions) undergoing abdominal, portal venous phase tbDECT were initially included in this retrospective study. Gold standard was pathological confirmation or follow-up imaging (MRI or multiphasic CT). CM, IOD, and ROI size were recorded. Thresholds for CM and IOD were identified using Youden-Index of the empirical ROC curves. Decision tree (DTC) and random forest classifier (RFC) were trained. Sensitivities, specificities, and AUCs were compared using McNemar and DeLong test., Results: The final study cohort comprised 40 enhancing and 113 non-enhancing renal lesions. Optimal thresholds for quantitative iodine measurements and contrast enhancement-based attenuation were 1.0 ± 0.0 mg/ml and 23.6 ± 0.3 HU, respectively. Single DECT parameters (IOD, CM) showed similar overall performance with an AUC of 0.894 and 0.858 (p = 0.541) (sensitivity 90 and 80%, specificity 88 and 92%, respectively). While overall performance for the DTC (AUC 0.944) was higher than RFC (AUC 0.886), this difference (p = 0.409) and comparison to CM (p = 0.243) and IOD (p = 0.353) was not statistically significant., Conclusions: Enhancement in incidental renal lesions on single-phase tbDECT can be classified with up to 87.5% sensitivity and 94.6% specificity. Algorithms combining DECT parameters did not increase overall performance.

Purpose: To evaluate the in vivo feasibility of a multibolus contrast agent (CA) injection protocol with a reduced CA volume for thoraco-abdominal CT angiography (CTA) and to compare it to a single-bolus CA injection protocol., Method: 63 patients who underwent CTA with the multibolus protocol (60 ml CA) were divided in two groups either without (group 1, n = 48) or with (group 2, n = 15) aortic dissection. The aortic contrast enhancement was measured in group 1 using manual ROI analysis (10 segments), as well as semi-automated linear attenuation profiles. A subgroup (n = 18) of group 1, who also underwent imaging with the single-bolus protocol (94 ml CA), was used to compare both protocols. In group 2, differences in attenuation of the true and the false lumen for both the single- and the multibolus protocol were assessed with ROI attenuation measurements in both lumina. Comparisons were made using Wilcoxon test., Results: Average attenuation was above 200 HU for 98 % of cases using the multibolus protocol. There was superior contrast homogeneity for the multibolus protocol with a lower standard deviation of attenuation values along the length of the scan (p = 0.003), while average attenuation was higher for the single-bolus protocol (p = 0.002). Prolonged enhancement plateau lead to a more uniform opacification of the true and the false lumen in patients with aortic dissection using the multibolus protocol (p = 0.012)., Conclusions: The multibolus protocol in thoraco-abdominal CTA is feasible in patients. It shows consistently high arterial enhancement with superior contrast homogeneity compared to a single-bolus protocol in patients with and without aortic dissection., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To evaluate the performance of an artificial intelligence (AI) based software solution tested on liver volumetric analyses and to compare the results to the manual contour segmentation., Materials and Methods: We retrospectively obtained 462 multiphasic CT datasets with six series for each patient: three different contrast phases and two slice thickness reconstructions (1.5/5 mm), totaling 2772 series. AI-based liver volumes were determined using multi-scale deep-reinforcement learning for 3D body markers detection and 3D structure segmentation. The algorithm was trained for liver volumetry on approximately 5000 datasets. We computed the absolute error of each automatically- and manually-derived volume relative to the mean manual volume. The mean processing time/dataset and method was recorded. Variations of liver volumes were compared using univariate generalized linear model analyses. A subgroup of 60 datasets was manually segmented by three radiologists, with a further subgroup of 20 segmented three times by each, to compare the automatically-derived results with the ground-truth., Results: The mean absolute error of the automatically-derived measurement was 44.3 mL (representing 2.37 % of the averaged liver volumes). The liver volume was neither dependent on the contrast phase (p = 0.697), nor on the slice thickness (p = 0.446). The mean processing time/dataset with the algorithm was 9.94 s (sec) compared to manual segmentation with 219.34 s. We found an excellent agreement between both approaches with an ICC value of 0.996., Conclusion: The results of our study demonstrate that AI-powered fully automated liver volumetric analyses can be done with excellent accuracy, reproducibility, robustness, speed and agreement with the manual segmentation., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: To investigate if supervised machine learning (ML) classifiers would be able to predict clinically significant cancer (sPC) from a set of quantitative image-features and to compare these results with established PI-RADS v2 assessment scores., Methods: We retrospectively included 201, histopathologically-proven, peripheral zone (PZ) prostate cancer lesions. Gleason scores ≤3+3 were considered as clinically insignificant (inPC) and Gleason scores ≥3+4 as sPC and were encoded in a binary fashion, serving as ground-truth. MRI was performed at 3T with high spatiotemporal resolution DCE using Golden-angle RAdial SParse (GRASP) MRI. Perfusion maps (Ktrans, Kep, Ve), apparent diffusion coefficient (ADC), and absolute T2-signal intensities (SI) were determined in all lesions and served as input parameters for four supervised ML models: Gradient Boosting Machines (GBM), Neural Networks (NNet), Random Forest (RF) and Support Vector Machines (SVM). ML results and PI-RADS scores were compared with the ground-truth. Next ROC-curves and AUC values were calculated., Results: All ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC (RF, GBM, NNet and SVM vs. PI-RADS: AUC 0.899, 0.864, 0.884 and 0.874 vs. 0.595, all P<0.001)., Conclusions: Using quantitative imaging parameters as input, supervised ML models outperformed PI-RADS v2 assessment scores in the prediction of sPC. These results indicate that quantitative imagining parameters contain relevant information for the prediction of sPC from image features., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims.2020.03.08). The authors have no conflicts of interest to declare., (2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background Gadoxetate disodium has been associated with various respiratory irregularities at arterial imaging MRI. Purpose To measure the relationship between gadolinium-based contrast agent administration and irregularities by comparing gadoxetate disodium and gadoterate meglumine at free breathing. Materials and Methods This prospective observational cohort study (January 2015 to May 2017) included consecutive abdominal MRI performed with either gadoxetate disodium or gadoterate meglumine enhancement. Participants underwent dynamic imaging by using the golden-angle radial sparse parallel sequence at free breathing. The quantitative assessment evaluated the aortic contrast enhancement, the respiratory hepatic translation, and the k-space-derived respiratory pattern. Analyses of variance compared hemodynamic metrics, respiratory-induced hepatic motion, and respiratory parameters before and after respiratory gating. Results A total of 497 abdominal MRI examinations were included. Of these, 338 participants were administered gadoxetate disodium (mean age, 59 years ± 15; 153 women) and 159 participants were administered gadoterate meglumine (mean age, 59 years ± 17; 85 women). The arterial bolus of gadoxetate disodium arrived later than gadoterate meglumine (19.7 vs 16.3 seconds, respectively; P < .001). Evaluation of the hepatic respiratory translation showed respiratory motion occurring in 70.7% (239 of 338) of participants who underwent gadoxetate-enhanced examinations and in 28.9% (46 of 159) of participants who underwent gadoterate-enhanced examinations ( P < .001). The duration of motion irregularities was longer for gadoxetate than for gadoterate (19.2 seconds vs 17.2 seconds, respectively) and the motion irregularities were more severe ( P < .001). Both the respiratory frequency and amplitude were shorter for participants administered gadoxetate from the prebolus phase to the late arterial phase compared with gadoterate ( P < .001). Conclusion The administration of two different gadolinium-based contrast agents, gadoxetate and gadoterate, at free-breathing conditions potentially leads to respiratory irregularities with differing intensity and onset. © RSNA, 2019 Online supplemental material is available for this article.

Objective: We aim to compare factors influencing safety, success rate and radiation dose of CT-guided biopsies and drainages in a non-teaching setting with experienced operators vs a teaching setting with residents., Methods: A total of 1021 cases were retrospectively analyzed regarding lesion size, distance from skin, procedure duration, radiation dose, complications and clinical success. Procedures were grouped into biopsies of lung, liver, (remaining) abdomen, musculoskeletal system (MSK) and drainages of any region. Procedures in non-teaching setting were performed by experienced operators (full time interventional radiology staff), teaching setting consisted of residents under supervision of interventional radiology staff., Results: Overall clinical success rate was 93.6 % [experienced (exp.) vs teaching setting: 93.5 and 93.6 %, p = 0.97]. Overall complication rate was 7.2% (5.7% minor, 1.6% major; exp. vs teaching: 8.0 and 6.5 %, p = 0.67]. Experienced operators performed chest and liver biopsies faster even though they were facing smaller lesions. Multiple regression analysis revealed that depth from skin significantly increased procedure duration by 36.8 s per cm ( p < 0.001) and also radiation dose by 5.4 mGy per cm ( p < 0.001) in all interventions. On average, teaching setting increased the duration of an intervention by 209.8 s and total radiation dose by 10.6 mGy ( p < 0.001, p < 0.001 respectively)., Conclusion: CT guided interventions can be performed safe und successful disregarding anatomical parameters or teaching setting. Depth from skin and teaching setting should be taken into account both from a clinical and a time-conscious point of view since they increase radiation dose and prolong operations., Advances in Knowledge: This is the first study with >1000 interventions which shows and quantifies the impact of lesion depth and teaching setting in CT-guided interventions.

Clinical/methodical Issue: In daily routine, every radiologist is confronted with a variety of questions concerning the biliary tract and pancreatic system., Standard Radiological Methods: Today, besides sonography, magnetic resonance cholangiopancreatography (MRCP) is considered the method of choice in the investigation of many disorders of the hepatobiliary and pancreatic system and is commonly preferred over invasive ERCP in a mere diagnostic setting., Methodical Innovations: Since its introduction in 1991, MRCP has constantly evolved. Major innovations have been the reduction of acquisition time by using fast spin echo (FSE) sequences, the use of respiratory gating and contrast-enhanced imaging of the bile ducts with hepatobiliary-specific MRI contrast agents., Performance: Many diagnoses may already be made with noncontrast enhanced images. By supplemental administration of a hepatobiliary-specific contrast agent, it is also possible to evaluate the flow dynamics of the bile. This is of additional value especially in patients who underwent surgery of the biliodigestive system or endoscopic interventions., Achievements: Aside from robustness and reproducibility, a major advantage of this technique is the modular design of imaging protocols, which can easily be adapted to the clinical question., Practical Recommendations: MRCP is a reliable and low-risk imaging method for primary diagnosis and follow-up of biliary and pancreatic pathologies.

Purpose To investigate the diagnostic performance of a dual-parameter approach by combining either volumetric interpolated breath-hold examination (VIBE)- or golden-angle radial sparse parallel (GRASP)-derived dynamic contrast agent-enhanced (DCE) MRI with established diffusion-weighted imaging (DWI) compared with traditional single-parameter evaluations on the basis of DWI alone. Materials and Methods Ninety-four male participants (66 years ± 7 [standard deviation]) were prospectively evaluated at 3.0-T MRI for clinical suspicion of prostate cancer. Included were 101 peripheral zone prostate cancer lesions. Histopathologic confirmation at MRI transrectal US fusion biopsy was matched with normal contralateral prostate parenchyma. MRI was performed with diffusion weighting and DCE by using GRASP (temporal resolution, 2.5 seconds) or VIBE (temporal resolution, 10 seconds). Perfusion (influx forward volume transfer constant [K trans ] and rate constant [K ep ]) and apparent diffusion coefficient (ADC) parameters were determined by tumor volume analysis. Areas under the receiver operating characteristic curve were compared for both sequences. Results Evaluated were 101 prostate cancer lesions (GRASP, 61 lesions; VIBE, 40 lesions). In a combined analysis, diffusion and perfusion parameters ADC with K trans or K ep acquired with GRASP had higher diagnostic performance compared with diffusion characteristics alone (area under the curve, 0.97 ± 0.02 [standard error] vs 0.93 ± 0.03; P < .006 and .021, respectively), whereas ADC with perfusion parameters acquired with VIBE had no additional benefit (area under the curve, 0.94 ± 0.03 vs 0.93 ± 0.04; P = .18and .50, respectively, for combination of ADC with K trans and K ep ). Conclusion If used in a dual-parameter model, incorporating diffusion and perfusion characteristics, the golden-angle radial sparse parallel acquisition technique improves the diagnostic performance of multiparametric MRI examinations of the prostate. This effect could not be observed combining diffusing with perfusion parameters acquired with volumetric interpolated breath-hold examination. © RSNA, 2018.

Background: Effective interdisciplinary communication of imaging findings is vital for patient care, as referring physicians depend on the contained information for the decision-making and subsequent treatment. Traditional radiology reports contain non-structured free text and potentially tangled information in narrative language, which can hamper the information transfer and diminish the clarity of the report. Therefore, this study investigates whether newly developed structured reports (SRs) of prostate magnetic resonance imaging (MRI) can improve interdisciplinary communication, as compared to non-structured reports (NSRs)., Methods: 50 NSRs and 50 SRs describing a single prostatic lesion were presented to four urologists with expert level experience in prostate cancer surgery or targeted MRI TRUS fusion biopsy. They were subsequently asked to plot the tumor location in a 2-dimensional prostate diagram and to answer a questionnaire focusing on information on clinically relevant key features as well as the perceived structure of the report. A validated scoring system that distinguishes between "major" and "minor" mistakes was used to evaluate the accuracy of the plotting of the tumor position in the prostate diagram., Results: The mean total score for accuracy for SRs was significantly higher than for NSRs (28.46 [range 13.33-30.0] vs. 21.75 [range 0.0-30.0], p < 0.01). The overall rates of major mistakes (54% vs. 10%) and minor mistakes (74% vs. 22%) were significantly higher (p < 0.01) for NSRs than for SRs. The rate of radiologist re-consultations was significantly lower (p < 0.01) for SRs than for NSRs (19% vs. 85%). Furthermore, SRs were rated as significantly superior to NSRs in regard to determining the clinical tumor stage (p < 0.01), the quality of the summary (4.4 vs. 2.5; p < 0.01), and overall satisfaction with the report (4.5 vs. 2.3; p < 0.01), and as more valuable for further clinical decision-making and surgical planning (p < 0.01)., Conclusions: Structured reporting of prostate MRI has the potential to improve interdisciplinary communication. Through SRs, expert urologists were able to more accurately assess the exact location of single prostate cancer lesions, which can facilitate surgical planning. Furthermore, structured reporting of prostate MRI leads to a higher satisfaction level of the referring physician., Competing Interests: The authors have declared that no competing interests exist.

Objective: The aim of this study was to test the diagnostic performance of a deep learning-based triage system for the detection of acute findings in abdominal computed tomography (CT) examinations., Materials and Methods: Using a RIS/PACS (Radiology Information System/Picture Archiving and Communication System) search engine, we obtained 100 consecutive abdominal CTs with at least one of the following findings: free-gas, free-fluid, or fat-stranding and 100 control cases with absence of these findings. The CT data were analyzed using a convolutional neural network algorithm previously trained for detection of these findings on an independent sample. The validation of the results was performed on a Web-based feedback system by a radiologist with 1 year of experience in abdominal imaging without prior knowledge of image findings through both visual confirmation and comparison with the clinically approved, written report as the standard of reference. All cases were included in the final analysis, except those in which the whole dataset could not be processed by the detection software. Measures of diagnostic accuracy were then calculated., Results: A total of 194 cases were included in the analysis, 6 excluded because of technical problems during the extraction of the DICOM datasets from the local PACS. Overall, the algorithm achieved a 93% sensitivity (91/98, 7 false-negative) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Intra-abdominal free gas was detected with a 92% sensitivity (54/59) and 93% specificity (39/42), free fluid with a 85% sensitivity (68/80) and 95% specificity (20/21), and fat stranding with a 81% sensitivity (42/50) and 98% specificity (48/49). False-positive results were due to streak artifacts, partial volume effects, and a misidentification of a diverticulum (each n = 1)., Conclusions: The algorithm's autonomous detection of acute pathological abdominal findings demonstrated a high diagnostic performance, enabling guidance of the radiology workflow toward prioritization of abdominal CT examinations with acute conditions.

Objective: The aim of the current study was to evaluate the reliability and comparability of virtual unenhanced (VUE) attenuation values derived from scans of a single-source, dual-energy computed tomography using a split-filter (tbDECT) to a dual-source dual-energy CT (dsDECT)., Materials and Methods: In this retrospective study, comparisons for tbDECT and dsDECT were made within and between different dual-energy platforms. For the interscanner comparison, 126 patients were scanned with both scanners within a time interval of 224 ± 180 days; for the intrascanner comparison, another 90 patients were scanned twice with the same scanner within a time interval of 136 ± 140 days. Virtual unenhanced images were processed off of venous phase series. Attenuation values of 7 different tissues were recorded. Disagreement for VUE HU measurements greater than 10 HU between 2 scans was defined as inadequate., Results: The interscanner analysis showed significant difference between tbDE and dsDE VUE CT values (P < 0.01) for 6 of 7 organs. Percentage of cases that had more than 10 HU difference between tbDE and dsDE for an individual patient ranged between 15% (left kidney) and 62% (spleen).The intrascanner analysis showed no significant difference between repeat scans for both tbDECT and dsDECT (P > 0.05). However, intrascanner disagreements for the VUE HU measurements greater than 10 HU were recorded in 10% of patients scanned on the tbDECT and 0% of patients scanned on the dsDECT. The organs with the highest portion of greater than 10 HU errors were the liver and the aorta (both 20%)., Conclusions: Dual-energy techniques vary in reproducibility of VUE attenuation values. In the current study, tbDECT demonstrated higher variation in VUE HU measurements in comparison to a dsDECT. Virtual unenhanced HU measurements cannot be reliably compared on follow-up CT, if these 2 different dual-energy CT platforms are used.