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Abstract Background This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs. Methods This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis. Results Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p

Abstract Background Thymus has a pivotal role in combating infectious diseases. Although some reviews have been published about its critical role in COVID-19, there is not enough research. In this study, the size and density of thymus related to computed tomography pulmonary severity score (CT-SS) were researched. Results A total of 196 patients were analyzed with a mean age of 52.54 ± 18.78 years; 97 (49.5%) of them were RT-PCR (−) and 99 (50.5%) were RT-PCR (+). Within RT-PCR (+) group 62 (62.6%) of them had pneumonia with a mean CT-SS of 9.37 ± 8.83; within RT-PCR (−) group 20 (20.6%) of them had pneumonia with the mean CT-SS of 12.00 ± 10.18. CT-SS had moderate negative correlation with thymus volume and thymus maximum diameter in patients having nodular-type thymus (R = −0.591, P = 0.02; R = −0.515, P = 0.049, respectively). Homogenous fat infiltration was more commonly seen in RT-PCR (−) group while reticular and nodular types were commonly seen in RT-PCR (+) group (p = 0.015). The mean volume and maximum diameter of thymus were statistically significantly higher in RT-PCR (+) group (p = 0.027 and p = 0.048, respectively). Conclusion This study showed the higher thymic volume and maximum diameter and more involution in COVID-19 patients. CT-SS had a moderate negative correlation with thymus volume and thymus maximum diameter. Pneumonia was more frequent in COVID patients, but mean CT-SS of the non-COVID cases was higher.

Abstract Background This study aimed to evaluate the artificial intelligence (AI)-based coronary artery calcium (CAC) quantification and regional distribution of CAC on non-gated chest CT, using standard electrocardiograph (ECG)-gated CAC scoring as the reference. Methods In this retrospective study, a total of 405 patients underwent non-gated chest CT and standard ECG-gated cardiac CT. An AI-based algorithm was used for automated CAC scoring on chest CT, and Agatston score on cardiac CT was manually quantified. Bland-Altman plots were used to evaluate the agreement of absolute Agatston score between the two scans at the patient and vessel levels. Linearly weighted kappa (κ) was calculated to assess the reliability of AI-based CAC risk categorization and the number of involved vessels on chest CT. Results The AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (κ = 0.75, 95% CI 0.70–0.79, P

Objective: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). Methods: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. Results: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640–0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. Conclusions: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT. [ABSTRACT FROM AUTHOR]

Purpose: Radiologically defined sarcopenia, or a low skeletal muscle index (SMI), is an emerging biomarker for adverse clinical outcomes in head and neck cancer (HNC) patients. Recently, SMI measurements have been validated at the level of the third cervical vertebra (C3) on diagnostic neck CT scans but are not yet validated on low-dose (LD) neck CT scans from the [18F]-FDG PET-CT. This hampers SMI analysis in HNC patients without a diagnostic neck CT but with a [18F]-FDG PET-CT scan. Therefore, the aim was to study whether (low) SMI based on LD CT scan from [18F]-FDG PET-CT is comparable to those derived from diagnostic neck CT scans. Methods: HNC patients with both diagnostic CT and [18F]-FDG PET-CT of the neck were prospectively included into the OncoLifeS data-biobank. Skeletal muscle was retrospectively delineated at the level of the third cervical vertebra (C3), and (low) SMI (cm2/m2) was calculated for diagnostic and LD neck CTs. (Low) SMI from the diagnostic neck CT was considered the reference standard. Intra-class correlation coefficient (ICC), Bland–Altman plots, and Cohen's Kappa analysis were performed. Results: The cohort (n = 233) mean age was 66.2 ± 12.8 years, and 74.2% of patients were male. Inter-rater reliability was excellent (ICC > 0.990, 95% confidence interval 0.975–0.996, p < 0.001). The agreement of SMI between both modalities was high according to the Bland–Altman plot (mean ΔSMI = − 0.19 cm2/m2), and there was no substantial bias. Cohen's Kappa analysis showed an almost perfect agreement of low SMI between the two modalities (κ = 0.911, p < 0.001). The position of arms didn't affect the high agreement of (low) SMI. Conclusion: Skeletal muscle mass, as measured with (low) SMI, remains constant irrespective of CT acquisition parameters (diagnostic neck CT scans versus LD neck scans of the [18F]-FDG PET-CT scan), positioning of arms, and observers. These findings contribute to the construction of a clinically useful radiological biomarker for SMI and therefore identify patients at risk for adverse clinical outcomes. [ABSTRACT FROM AUTHOR]

Computed tomography has become a critical component in evaluating adult patients with acute caustic ingestions and an alternative to endoscopy for detecting transmural gastrointestinal necrosis. This study assessed the performance and reliability of computed tomography findings of transmural gastrointestinal necrosis, given that the presence of the disease potentially signifies the need for surgery. A retrospective database search was performed to identify consecutive adult patients with acute caustic ingestions who had computed tomography with endoscopy or surgery within 72 h of admission. Eight physicians reinterpreted computed tomography in two separate rounds. Diagnostic performance utilized eight rounds of radiologists' reinterpretations against reference endoscopic or surgical grades. Intra- and interobserver agreements were calculated. Seventeen patients (mean age, 45.6 years; 9 men; 46 esophageal and 34 gastric segments; 16 ingested strong acid substances) met the inclusion criteria. Eight patients (10 esophageal and 13 gastric segments) had transmural gastrointestinal necrosis. The highly differentiating findings between those with and without transmural gastrointestinal necrosis were esophageal wall thickening (100% vs. 42%, P = 0.001; 100% sensitive), gastric abnormal wall enhancement and fat stranding (100% vs. 57%, P = 0.006; 100% sensitive), and gastric absent wall enhancement (46% vs. 5%, P = 0.007; 100% specific). The intra- and interobserver percentage agreements were 47–100%, and 54–100%, which increased to 53–100%, and 60–100%, respectively, when considering only radiologists' reinterpretations. In a very small sample of adults who primarily ingested acid, contrast-enhanced computed tomography performed well when interpreted by a panel of radiologists. [ABSTRACT FROM AUTHOR]

Objective: To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC). Methods: Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis. Results: Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p =.003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p <.001) than the SD protocol. The comparative analysis demonstrated that CNR (p <.001) and portal vein conspicuity (p =.002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p =.140) and HCCs (75.7% vs. 70.4%, p =.644) between the SD protocol and DLD-DL. Conclusions: DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC. Key Points: • Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality. [ABSTRACT FROM AUTHOR]

Objective: Reproducibility problems are a known limitation of radiomics. The segmentation of the target lesion plays a critical role in texture analysis variability. This study's aim was to compare the interobserver reliability of manual 2D vs. 3D lung lesion segmentation with and without pre-definition of the volume using a threshold of − 50 HU. Methods: Seventy-five patients with histopathologically proven lung lesions (15 patients each with adenocarcinoma, squamous cell carcinoma, small cell lung cancer, carcinoid, and organizing pneumonia) who underwent an unenhanced CT scan of the chest were included. Three radiologists independently segmented each lesion manually in 3D and 2D with and without pre-segmentation volume definition by a HU threshold, and shape parameters and original, Laplacian of Gaussian–filtered, and wavelet-based texture features were derived. To assess interobserver reliability and identify the most robust texture features, intraclass correlation coefficients (ICCs) for different segmentation settings were calculated. Results: Shape parameters had high reliability (64–79% had excellent and good ICCs). Texture features had weak reliability levels, with the highest ICCs (38% excellent or good) found for original features in 3D segmentation without the use of a HU threshold. A small proportion (4.3–11.5%) of texture features had excellent or good ICC values at all segmentation settings. Conclusion: Interobserver reliability of texture features from CT scans of a heterogeneous collection of manually segmented lung lesions was low with a small proportion of features demonstrating high reliability independent of the segmentation settings. These results indicate a limited applicability of texture analysis and the need to define robust texture features in patients with lung lesions. Key Points: • Our study showed a low reproducibility of texture features when 3 radiologists independently segmented lung lesions in CT images, which highlights a serious limitation of texture analysis. • Interobserver reliability of texture features was low regardless of whether the lesion was segmented in 2D and 3D with or without a HU threshold. • In contrast to texture features, shape parameters showed a high interobserver reliability when lesions were segmented in 2D vs. 3D with and without a HU threshold of − 50. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the feasibility and image quality of intranodal dynamic contrast-enhanced CT lymphangiography (DCCTL) and dynamic contrast-enhanced MR lymphangiography (DCMRL) in microminipigs. Methods: Our institution's committee for animal research and welfare provided approval. Three microminipigs underwent DCCTL and DCMRL after inguinal lymph node injection of 0.1 mL/kg contrast media. Mean CT values on DCCTL and signal intensity (SI) on DCMRL were measured at the venous angle and thoracic duct (TD). The contrast enhancement index (CEI; increase in CT values pre- to post-contrast) and signal intensity ratio (SIR; SI of lymph divided by SI of muscle) were evaluated. The morphologic legibility, visibility, and continuity of lymphatics were qualitatively evaluated using a 4-point scale. Two microminipigs underwent DCCTL and DCMRL after lymphatic disruption and the detectability of lymphatic leakage was evaluated. Results: The CEI peaked at 5–10 min in all microminipigs. The SIR peaked at 2–4 min in two microminipigs and at 4–10 min in one microminipig. The peak CEI and SIR values were 235.6 HU and 4.8 for venous angle, 239.4 HU and 2.1 for upper TD, and 387.3 HU and 2.1 for middle TD. The visibility and continuity of upper–middle TD scores were 4.0 and 3.3–3.7 for DCCTL, and 4.0 and 4.0 for DCMRL. In the injured lymphatic model, both DCCTL and DCMRL demonstrated lymphatic leakage. Conclusions: DCCTL and DCMRL in a microminipig model enabled excellent visualization of central lymphatic ducts and lymphatic leakage, indicating the research and clinical potential of both modalities. Key Points: • Intranodal dynamic contrast-enhanced computed tomography lymphangiography showed a contrast enhancement peak at 5–10 min in all microminipigs. • Intranodal dynamic contrast-enhanced magnetic resonance lymphangiography showed a contrast enhancement peak at 2–4 min in two microminipigs and at 4–10 min in one microminipig. • Both intranodal dynamic contrast-enhanced computed tomography lymphangiography and dynamic contrast-enhanced magnetic resonance lymphangiography demonstrated the central lymphatic ducts and lymphatic leakage. [ABSTRACT FROM AUTHOR]

Objectives: To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists. Methods: A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy. Results: The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847–0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882–0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737–0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779–0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793–0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673–0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05). Conclusions: The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert. Key Points: • Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the predictive value of CT-derived fractional flow reserve (FFRCT) in anastomosis occlusion after coronary artery bypass graft (CABG) surgery. Methods: Patients undergoing CABG with both pre- and post-operative coronary computed tomographic angiography (CCTA) were retrospectively included. Preoperative CCTA studies were used to evaluate anatomical and FFRCT information of target vessels. A diameter stenosis (DS) ≥ 70% or left main > 50% was considered to be anatomically severe, while FFRCT value ≤ 0.80 be functionally significant. The primary endpoint was anastomosis occlusion evaluated on post-operative CCTA during follow-up. Predictors of anastomosis occlusion were assessed by the multivariate binary logistic regression with generalized estimating equations. Results: A total of 270 anastomoses were identified in 88 enrolled patients. Forty-one anastomoses from 30 patients exhibited occlusion during a follow-up of 15.3 months after CABG. The occluded group had significantly increased prevalence of non-severe DS (58.5% vs. 40.2%; p = 0.023) and non-significant FFRCT (48.8% vs. 10.0%; p < 0.001). Multivariable analysis indicated FFRCT ≤ 0.80 (odds ratio [OR]: 0.10, 95% CI: 0.03–0.33; p < 0.001) and older age (OR: 0.92, 95% CI: 0.87–0.97; p = 0.001) were predictors for bypass patency during follow-up, while myocardial infarction history and anastomosis to a local lesion or bifurcation (all p value < 0.05) were predictors of occlusion. Adding FFRCT into the model based on the clinical and anatomical predictors had an improved AUC of 0.848 (p = 0.005). Conclusions: FFRCT ≤ 0.80 was associated with a significant risk reduction of anastomosis occlusion after CABG. Preoperative judgment of the hemodynamic significance may improve the CABG surgery strategy and reduce graft failure. Key Points: • FFRCT≤ 0.80 was associated with a significant risk reduction of anastomosis occlusion after CABG. • The addition of FFRCTinto the integrated model including clinical (age and history of myocardial infarction) and anatomical CCTA indicators (local lesion and bifurcation) significantly improved the model performance with an AUC of 0.848 (p = 0.005). • Preoperative judgment of the hemodynamic significance may help improve the decision-making and surgery planning in patients indicated for CABG and significantly reduce graft failure, without an extra radiation exposure and risk of invasive procedure. [ABSTRACT FROM AUTHOR]

Objectives: Polycystic liver disease (PLD) is characterized by growth of hepatic cysts, causing hepatomegaly. Disease severity is determined using total liver volume (TLV), which can be measured from computed tomography (CT). The gold standard is manual segmentation which is time-consuming and requires expert knowledge of the anatomy. This study aims to validate the commercially available semi-automatic MMWP (Multimodality Workplace) Volume tool for CT scans of PLD patients. Methods: We included adult patients with one (n = 60) or two (n = 46) abdominal CT scans. Semi-automatic contouring was compared with manual segmentation, using comparison of observed volumes (cross-sectional) and growth (longitudinal), correlation coefficients (CC), and Bland-Altman analyses with bias and precision, defined as the mean difference and SD from this difference. Inter- and intra-reader variability were assessed using coefficients of variation (CV) and we assessed the time to perform both procedures. Results: Median TLV was 5292.2 mL (IQR 3141.4–7862.2 mL) at baseline. Cross-sectional analysis showed high correlation and low bias and precision between both methods (CC 0.998, bias 1.62%, precision 2.75%). Absolute volumes were slightly higher for semi-automatic segmentation (manual 5292.2 (3141.4–7862.2) versus semi-automatic 5432.8 (3071.9–7960.2) mL, difference 2.7%, p < 0.001). Longitudinal analysis demonstrated that semi-automatic segmentation accurately measures liver growth (CC 0.908, bias 0.23%, precision 4.04%). Inter- and intra-reader variability were small (2.19% and 0.66%) and comparable to manual segmentation (1.21% and 0.63%) (p = 0.26 and p = 0.37). Semi-automatic segmentation was faster than manual tracing (19 min versus 50 min, p = 0.009). Conclusions: Semi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients. Key Points: • Semi-automatic liver segmentation using the commercially available MMWP volume tool accurately determines total liver volume as well as liver growth over time in polycystic liver disease patients. • This method is considerably faster than manual segmentation through the use of Hounsfield unit settings. • We used a real-life CT set for the validation and showed that the semi-automatic tool measures accurately regardless of contrast used for the CT scan or not, presence of polycystic kidneys, liver volume, and previous invasive treatment for polycystic liver disease. [ABSTRACT FROM AUTHOR]

Objectives: In many countries, workers who developed asbestosis due to their occupation are eligible for government support. Based on the results of clinical examination, a team of pulmonologists determine the eligibility of patients to these programs. In this Dutch cohort study, we aim to demonstrate the potential role of an artificial intelligence (AI)-based system for automated, standardized, and cost-effective evaluation of applications for asbestosis patients. Methods: A dataset of n = 523 suspected asbestosis cases/applications from across the Netherlands was retrospectively collected. Each case/application was reviewed, and based on the criteria, a panel of three pulmonologists would determine eligibility for government support. An AI system is proposed, which uses thoracic CT images as input, and predicts the assessment of the clinical panel. Alongside imaging, we evaluated the added value of lung function parameters. Results: The proposed AI algorithm reached an AUC of 0.87 (p < 0.001) in the prediction of accepted versus rejected applications. Diffusion capacity (DLCO) also showed comparable predictive value (AUC = 0.85, p < 0.001), with little correlation between the two parameters (r-squared = 0.22, p < 0.001). The combination of the imaging AI score and DLCO achieved superior performance (AUC = 0.95, p < 0.001). Interobserver variability between pulmonologists on the panel was estimated at alpha = 0.65 (Krippendorff's alpha). Conclusion: We developed an AI system to support the clinical decision-making process for the application to the government support for asbestosis. A multicenter prospective validation study is currently ongoing to examine the added value and reliability of this system alongside the clinic panel. Key Points: • Artificial intelligence can detect imaging patterns of asbestosis in CT scans in a cohort of patients applying for state aid. • Combining the AI prediction with the diffusing lung function parameter reaches the highest diagnostic performance. • Specific cases with fibrosis but no asbestosis were correctly classified, suggesting robustness of the AI system, which is currently under prospective validation. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to identify the diagnostic accuracy of combined ultrasonography (US) and computed tomography (CT) in evaluating the tumor burden of pseudomyxoma peritonei (PMP). Besides, we assessed the ability of this combination to predict the likelihood of complete resection. Methods: This retrospective study involved 504 patients diagnosed with PMP and scheduled for cytoreduction surgery. We compared tumor burden—quantified as peritoneal cancer index (PCI) by preoperative US and CT (US-CT-PCI)—with surgical findings. Next, we assessed the prognostic value of US-CT PCI and imaging features in determining the completeness of cytoreduction (CCR) score using multivariate analysis. Results: US-CT PCI demonstrated a high PCI evaluation accuracy under moderate tumor burden. Higher US-CT PCI could predict incomplete resection. In addition, we identified imaging features such as mesenteric involvement as an independent predictor of incomplete resection (hazard ratio (HR) = 2.006; p = 0.007). Conclusions: US-CT PCI allowed us to predict the completeness of cytoreductive surgery in patients with PMP. Moreover, the combined US and CT imaging detected several features indicating incomplete cytoreduction. Key Points: • Ultrasonography (US) can act as a complementary diagnostic modality in peritoneal cancer index (PCI) evaluation by combining CT in the small bowel area and US in the abdominal area. • A modified peritoneal cancer index (US-CT PCI) helps preoperatively evaluate tumor burden with high accuracy and allows to predict incomplete resection. • US-CT PCI of 20 or above and the involvement of particular structures such as mesentery, independently indicate incomplete resection. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this retrospective study was to predict circumferential resection margin (CRM) involvement on preoperative CT, and prognostic impact of CRM assessment by CT (ctCRM) in patients with retroperitonealized colon cancer. Methods: This study included patients who underwent resection for ascending or descending colon cancer between July 2010 and February 2013. Positive ctCRM was defined as tumor distance to the retromesenteric plane of ≤ 1 mm. The origin of positive CRM was divided into primary tumor or other tumor components including lymph nodes, tumor deposits, or extramural venous invasions. Logistic regression analysis was performed to identify preoperative factors to predict pathologic CRM (pCRM). A Cox proportional hazards model was used in multivariable analysis to determine the preoperative factors affecting disease-free survival (DFS). Results: A total of 274 patients (mean age, 64.0 years ± 11.0 [standard deviation]; 157 men) with retroperitonealized colon cancer were evaluated. Of 274 patients, 67 patients (24.5%) had positive CRM on surgical pathology. The accuracy of preoperative CT in predicting pCRM was 79.6% (218/274). Among preoperative factors, only CRM assessment on CT was independently associated with pCRM (p < 0.001). Positive ctCRM by primary tumor was an independent factor for DFS (HR, 3.362 [1.714–6.593]) and systemic recurrence (HR, 3.715 [1.787–7.724], but not for local recurrence on multivariable analyses. Conclusions: Preoperative CT can accurately predict pCRM, and positive ctCRM by primary tumor is an independent risk factor for DFS and systemic recurrence, but not for local recurrence in retroperitonealized colon cancer. Key Points: • Preoperative CT can predict pathologic circumferential resection margin (CRM) with approximately 80% of accuracy in patients with retroperitonealized colon cancer. • Positive CRM by a primary tumor on preoperative CT is a poor prognostic factor for disease-free survival and systemic recurrence in patients with retroperitonealized colon cancer. • CRM involvement on CT was not associated with local recurrence in patients with retroperitonealized colon cancer. [ABSTRACT FROM AUTHOR]

Objectives: Facial canal dehiscence (FCD), typically found in the tympanic segment, is a risk factor for facial nerve injury. An imaging scoring method was proposed to identify FCD based on ultra-high-resolution CT. Methods: Forty patients (21 females and 19 males, mean age 44.3 ± 17.4 years), whose tympanic facial canal (FC) was examined during otological surgery, were divided into the FCD group (n = 29) and the control group (n = 11) based on surgical findings. Imaging appearance of tympanic FC was scored 0–3: 0 = no evident bony covering, 1 = discontinuous bony covering with linear deficiency, 2 = discontinuous bony covering with dotted deficiency, and 3 = continuous bony covering. Both lateral and inferior walls were assigned a score as LFCD and IFCD, respectively. An FCD score was calculated as LFCD + IFCD. The diagnostic value of the FCD score was tested using the ROC curve. Results: The inter-observer agreement was moderate for the lateral wall (Cohen's κ coefficient 0.416, 95% CI 0.193–0.639), and good for the inferior wall (Cohen's κ coefficient 0.702, 95% CI 0.516–0.888). In the FCD group, the most common appearance for both walls was discontinuous bony covering with linear deficiency (LFCD = 1, 22/29, 75.9%; IFCD = 1, 15/29, 51.7%). An FCD score of less than 4 was associated with high sensitivity (0.82) and specificity (0.93) for identifying FCD, with an AUC of 0.928. Conclusions: Using the proposed scoring method, FCD score < 4 could identify FCD of the tympanic segment with high concordance with surgical findings. Key Points: • Imaging appearance of the tympanic facial canal (FC) is divided into four types based on ultra-high-resolution CT images. • The most common appearance of FC with facial canal dehiscence (FCD) is discontinuous bony covering with linear deficiency. • An FCD score, consisting of scores of the lateral and inferior walls, less than 4 is highly indicative of FCD. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1–2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. Methods: A total of 151 pathologically confirmed 1–2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. Results: The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1–2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1–2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. Conclusions: Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1–2-cm gGISTs. Key Points: • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1–2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1–2-cm gGISTs. [ABSTRACT FROM AUTHOR]

Background: Coronary artery calcification (CAC) burden assessed by Agatston score (AS) is currently recommended to stratify patients at risk for future acute coronary syndrome (ACS). Besides the CAC burden, the biostructure of CAC may also play a vital role in the vulnerability of CAC, which CT radiomics could reveal. Propensity-score matching of the traditional risk factors and CAC burden between the ACS and asymptomatic groups could radically remove biases and allow the exploration of characteristic features of CAC in ACS. Methods: We retrospectively identified 77 patients with ACS who had a CAC scan before percutaneous coronary intervention between 2016 and 2019. These 77 patients were one-to-two propensity-score matched for traditional risk factors of ACS and AS ranks to select 154 subjects from 2890 asymptomatic subjects. A validation cohort of 30 subjects was also enrolled. Radiomics features of each plaque were extracted and averaged in each person. Conditional logistic regression and area-under-curve analysis were used for statistical analysis. Results: A higher number of coronary segments involved, lower mean, median, first quartile, and standard deviation of attenuation, and increased kurtosis of attenuation of CAC were associated with the ACS group compared to the control group (p < 0.05 for all). Multivariable analysis showed that the lower median attenuation (OR = 0.969, p < 0.001) and higher Kurtosis (OR = 18.7, p < 0.001) were associated with the ACS group. The median attenuation and kurtosis significantly increase across AS ranks 1 to 4 (p = 0.001). The AUC of kurtosis (0.727) and median attenuation (0.66) were both significantly higher than that of the standard AS (AUC = 0.502) and the number of TRF (AUC = 0.537). The best cut-off of kurtosis at 2.74 yielded an accuracy of 74%, and the cut-off of median attenuation at 196 yielded an accuracy of 68%. The accuracy of kurtosis was 64%, and the accuracy of median attenuation was 55% in the validation cohort. Conclusion: After propensity-matching traditional risk factors and CAC burden, CT radiomics highlighted that lower median attenuation and higher kurtosis were the CAC characteristics of vulnerable plaques. These features improve the understanding of the biomechanics of CAC evolution and enhance the value of CAC scan in ACS risk assessment. [ABSTRACT FROM AUTHOR]

Background: We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). Methods: A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). Results: Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996–0.999) and 0.946 (95% CI, 0.906–0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. Conclusion: The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination. [ABSTRACT FROM AUTHOR]

Introduction. An isolated fracture of the small tuberosity is a very rare injury that can often be overlooked. That injury is significant due to the function of the subscapularis muscle, which loses its attachment by separating the small tuberosity. Case report. A 45-year-old male, former athlete, suffered a left shoulder injury while falling downhill. Although the clinical picture was not convincing, a computed tomography scan showed a fracture. Open repositioning and osteosynthesis were performed. After rehabilitation, the patient regained a full active range of motion in the shoulder joint three months after the operation. We present the mechanism of injury, clinical picture, necessary diagnostics, and technique of surgical treatment of isolated fracture lesser tuberosity of the humerus. The results of other authors whose series are also quite small were analyzed. Conclusion. It was concluded that an isolated fracture of the small tuberosity requires surgical treatment to preserve a good range of motion in the shoulder joint. [ABSTRACT FROM AUTHOR]

Objective To analyze quantitative indexes of emphysema and airway remodeling in patients with chronic obstructive pulmonary disease (COPD) by using high resolution CT (HRCT), and to explore the correlation between the above indexes and pulmonary function. Methods A total of 109 COPD patients (the COPD group) and 33 normal controls (the control group) were collected. According to the COPD Global Initiative guidelines, COPD patients were divided into the GOLD class Ⅰ group (n=29), the GOLD class Ⅱ group (n=38) and the GOLD class Ⅲ + Ⅳ group (n=42). All patients underwent HRCT examination and pulmonary function examination. The post-processing software was used to automatically measure HRCT quantitative emphysema indexes of each lung lobe, including emphysema index (EI), mean lung density (MLD) of each lobe and airway quantitative indexes [bronchial wall area (WA), bronchial lumen area (LA) and the percentage of the wall area in the total bronchial section area (WA%)]. The above parameters were compared between the normal group and the different severity COPD subgroups. Spearman method was used to analyze the correlation between the above CT quantitative indexes and pulmonary function. Results Compared with the normal group, EI in upper lobe of both lungs was increased in the GOLD class Ⅰ group, EI in all lobes was increased in the GOLD class Ⅱ group, MLD values in upper lobe of both lungs and lower lobe of left lung were decreased, EI in all lobes was increased in the GOLD class Ⅲ+Ⅳ group, and MLD was decreased (P＜0.05). Compared with the normal group, the WA of grade 5 bronchus and WA% of grade 4-6 bronchus were increased in the GOLD class Ⅰ group, and LA of grade 5 and 6 bronchus decreased. Compared with the GOLD class Ⅰ group, only WA and WA% of grade 6 bronchi were increased in the GOLD class Ⅱ group, and LA was decreased. Compared with the GOLD class Ⅱ group, WA% of grade 5 bronchial was decreased in the GOLD class Ⅲ+Ⅳ group (P＜0.05). The measured and predicted percentages of forced expiratory volume in the first second (FEV1%) were negatively correlated with the EI of each lung lobe, and the MLD was positively correlated with FEV1% (P＜0.01). The WA% of 5-6 bronchi was negatively correlated with FEV1% (P＜0.05). Conclusion HRCT emphysema quantitative indexes and airway quantitative indexes have certain correlation with pulmonary function. It varies with different COPD grades, which can provide reference value for COPD evaluation. [ABSTRACT FROM AUTHOR]

Objectives: To develop a CT-based algorithm and evaluate its performance for the diagnosis of blunt bowel and/or mesenteric injury (BBMI) in patients with blunt abdominal trauma. Methods: This retrospective study included a training cohort of 79 patients (29 with BBMI and 50 patients with blunt abdominal trauma without BBMI) and a validation cohort of 37 patients (13 patients with BBMI and 24 patients with blunt abdominal trauma without BBMI). CT examinations were blindly analyzed by two independent radiologists. For each CT sign, the kappa value, sensitivity, specificity, and accuracy were calculated. A diagnostic algorithm was built using a recursive partitioning model on the training cohort, and its performances were assessed on the validation cohort. Results: CT signs with kappa value > 0.6 were extraluminal gas, hemoperitoneum, no or moderate bowel wall enhancement, and solid organ injury. CT signs yielding best accuracies in the training cohort were extraluminal gas (98%; 95% CI: 91–100), bowel wall defect (97%; 95% CI: 91–100), irregularity of mesenteric vessels (97%; 95% CI: 90–99), and mesenteric vessel extravasation (97%; 95% CI: 90–99). Using a recursive partitioning model, a decision tree algorithm including extraluminal gas and no/moderate bowel wall enhancement was built, achieving 86% sensitivity (95% CI: 74–99) and 96% specificity (95% CI: 91–100) in the training cohort and 92% sensitivity (95% CI: 78–97) and 88% specificity (95% CI: 74–100) in the validation cohort for the diagnosis of BBMI. Conclusions: An effective diagnostic algorithm was built to identify BBMI in patients with blunt abdominal trauma using only extraluminal gas and no/moderate bowel wall enhancement on CT examination. Key Points: • A CT diagnostic algorithm that included extraluminal gas and no/moderate bowel wall enhancement was built for the diagnosis of surgical blunt bowel and/or mesenteric injury. • A decision tree combining only two reproducible CT signs has high diagnostic performance for the diagnosis of surgical blunt bowel and/or mesenteric injury. [ABSTRACT FROM AUTHOR]

Objectives: Compare the diagnostic performance of the arterial phase plus portovenous phases (AP + PVP) of abdominopelvic CT (CT) with PVP alone in the detection and characterization of traumatic vascular injury and the effects on radiologists' confidence. Methods: CT of 103 consecutive inpatients (median 36 years, 83 males) with blunt abdominopelvic injuries were retrospectively included if performed within 24 h after trauma and before definitive management. Images were re-reviewed by two blinded radiologists with disagreements resolved by the third radiologist. Results: Sixty vascular injuries (liver 23, spleen 15, kidneys 9, pancreas 2, adrenals 3, mesentery, and pelvis 4 each) were found with 4 injuries (liver 2, spleen, and kidneys 1 each) not detected at initial CT. Nineteen (liver 6, spleen 10, kidneys 2, adrenal 1) were visualized only on AP. The sensitivity and accuracy of AP + PVP were 89.58–91.67% and 94.44–95.15%, compared to 61.67–62.50% and 77.67–80.00% of PVP alone. The agreements on the types of injury with final diagnoses were higher for AP + PVP than for PVP alone (78.69% vs. 44.26%). The mean diagnostic radiologist confidence ((1 = 25%, 2 = 50%, 3 = 75%, 4 > 90%) increased significantly in the detection (from 3.38 to 3.71) and characterization (from 2.46 to 3.67) of vascular injuries with AP + PVP compared to PVP alone. For 19 lesions detected only on AP, 11 (spleen 8, liver 2, adrenal 1) received nonoperative management; others had transarterial embolization or surgery. Conclusions: The addition of AP improves the detection and characterization of vascular injuries in CT evaluation of blunt abdominopelvic trauma. Key Points: • AP+PVP was more sensitive and precise than PVP alone in the detection of traumatic vascular abdominopelvic injuries. • AP+PVP improved the characterization of traumatic abdominopelvic vascular injuries. • When all abdominopelvic vascular injuries were considered, AP increased radiologists' diagnostic confidence in the detection and characterization of vascular injuries. [ABSTRACT FROM AUTHOR]

Objectives: To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4–19), while all of included studies were assessed as having a high risk of bias (ROB) overall. Conclusions: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. Key Points: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4–19), and all of studies were considered to have a high risk of bias overall. [ABSTRACT FROM AUTHOR]

Objectives: To construct and validate a contrast-enhanced computed tomography (CECT)–based radiomics nomogram to predict Ki-67 expression level in head and neck squamous cell carcinoma (HNSCC). Methods: A total of 217 patients with HNSCC who underwent CECT scans and immunohistochemical examination of their Ki-67 index were enrolled in this study. The patients were divided into a training set (n = 140; Ki-67: ≥ 50% [n = 72] and < 50% [n = 68]) and an external test set (n = 77; Ki-67: ≥ 50% [n = 38] and < 50% [n = 39]). The least absolute shrinkage and selection operator method was used to select key features for a CECT-image-based radiomics signature and a radiomics score (Rad-score) was calculated. A clinical model was established using clinical data and CT findings. The independent clinical factors and Rad-score were then combined to construct a radiomics nomogram. The performance characteristics of the Rad-score, clinical model, and nomogram were assessed using ROCs and decision curve analysis. Results: Twenty features were finally selected to construct the Rad-score. The radiomics nomogram incorporating the Rad-score, low histological grade, and lymphatic spread showed higher predictive value for the Ki-67 index (≥ 50% vs. < 50%) than the clinical model on both the training (AUC, 0.919 vs. 0.648, p < 0.001) and test (AUC, 0.832 vs. 0.685, p = 0.030) sets. Decision curve analysis demonstrated that the radiomics nomogram was more clinically useful than the clinical model. Conclusions: A CECT-based radiomics nomogram was constructed to predict the expression of Ki-67 in HNSCC. This model showed favorable predictive efficacy and might be useful for prognostic evaluation and clinical decision-making in patients with HNSCC. Key Points: • Accurate pre-treatment prediction of Ki-67 index in HNSCC is crucial. • A CECT-based radiomics nomogram showed favorable predictive efficacy in estimation of Ki-67 expression status in HNSCC patients. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to demonstrate the effectiveness of spectral photon-counting CT (SPCCT) in quantifying fibrous cap (FC) thickness, FC area, and lipid-rich necrotic core (LRNC) area, in excised carotid atherosclerotic plaques by comparing it with histopathological measurements. Methods: This is a single-center ex vivo cross-sectional observational study. Excised plaques of 20 patients (71 +/- 6 years; 13 men), obtained from carotid endarterectomy were scanned with SPCCT using standardized acquisition settings (120k Vp/19 μA; 7–18 keV, 18–30 keV, 30–45 keV, 45–75 keV, and 75–118 keV). FC thickness, FC area, and LRNC area were quantified and compared between high-resolution 3D multi-energy CT images and histopathology using the Wilcoxon signed-ranks test and Bland–Altman analysis. Images were interpreted twice by two radiologists separately, blinded to the histopathology; inter- and intra-rater reliability were assessed with the intra-class correlation coefficients (ICC). Results: FC thickness and FC area did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements (p value range 0.15–0.51 for FC thickness and 0.053–0.30 for FC area). For the LRNC area, the p value was statistically non-significant for reader 1 (range 0.36–0.81). The Bland-Altman analysis showed mean difference and 95% confidence interval for FC thickness, FC area, and LRNC area, 0.04 (−0.36 to 0.12) square root mm, −0.18 (−0.34 to −0.02) log10 mm2 and 0.10 (−0.088. to 0.009) log10 mm2 respectively. Conclusion: The result demonstrated a viable technique for quantifying FC thickness, FC area, and LRNC area due to the combined effect of high spatial and energy resolution of SPCCT. Key Points: • SPCCT can identify and quantify different components of carotid atherosclerotic plaque in ex vivo study. • Components of atherosclerotic plaque did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate commercial deep learning–based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). Methods: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. Results: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, −244.0, 283.9] vs. 45.26 [−248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). Conclusions: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. Key Points: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mmdemonstrated excellent agreement with CSCTmanual(all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mmthan with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mmthan with LDCT2.5-mm. [ABSTRACT FROM AUTHOR]

Objectives: To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT. Methods: A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle–Ottawa scale, and publication bias was assessed by Egger's test. Results: Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15–29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12–39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3–7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth. Conclusions: The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth. Key Points: • Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective: diagnostic errors during the interpretation of an imaging test by the physician can lead to increased mortality and length of hospital stay for patients. The rate of divergence in the report given by a radiologist and an Emergency Physicians (EP) can reach over 20%. The objective of this study was to compare the unofficial tomographic reports issued by EP with the official reports issued by radiologists. Methods: a cross-sectional study, in which interpretations of the exams (documented in the medical records by the EP) of all patients undergoing computed tomography (CT) of the chest, abdomen or pelvis performed in the emergency room, at an interval of 8 months, were evaluated. These data were compared with the official reports of the radiologist (gold standard). Results: 508 patients were included. The divergence between EP and the radiologist occurred in 27% of the cases. The most common type of divergence was the one not described by the EP, but described by the radiologist. The chance of having divergence in a case of multiple trauma is 4.93 times greater in relation to the case of only blunt trauma in one segment. A statistically relevant difference was also found in the length of stay of patients who had different interpretations of the CT scans. Conclusion: the study found a relatively high divergence rate between the EP report and the official radiologist report. However, less than 4% of these were considered to be clinically relevant, indicating the ability of the EP to interpret it satisfactorily.

Introduction. Leiomyoma is a mesenchymal benign tumor that seldom manifests in the kidney. Typically, these tumors are small, asymptomatic, and often detected accidentally. Differentiating leiomyomas radiologically from other renal neoplasms, particularly renal cell carcinomas, poses a considerable challenge before surgical intervention. Moreover, the conclusive diagnosis of leiomyomas can only be established through histopathological and immunohistochemical evaluation following surgical intervention. Case Report. We present a case of a 44-year-old woman who was diagnosed with right kidney leiomyoma accidentally during a computed tomography examination following exploratory curettage by a gynecologist. An enhanced computed tomography scan revealed a 10 mm tumor located on the upper pole of the right kidney, extending beyond its boundaries. The imaging differential diagnosis was renal cell carcinoma of the right kidney. A laparoscopic partial nephrectomy was performed. Macroscopically, the tumor appeared subcapsular, well-defined, with a firm consistency and a tan-white whorled cut surface. The histological characteristics and the supporting immunohistochemical profile confirmed the diagnosis of renal leiomyoma. The postoperative course was uneventful, and the patient was discharged on the 3rd postoperative day. Conclusion. Leiomyomas are rare kidney tumors. A conclusive diagnosis is established through histopathological findings. The pre-operative differentiation of leiomyomas radiologically from other renal neoplasms, particularly renal cell carcinomas, poses a considerable challenge in radiological assessment. [ABSTRACT FROM AUTHOR]

Background: The effect of region of interest (ROI) size variation on producing accurate noise levels is not yet studied. Objective: This study aimed to evaluate the influence of ROI sizes on the accuracy of noise measurement in computed tomography (CT) by using images of a computational and American College of Radiology (ACR) phantoms.Material and Methods: In this experimental study, two phantoms were used, including computational and ACR phantoms. A computational phantom was developed by using Matlab R215a software (Mathworks Inc., Natick, MA Natick, MA) with a homogeneously +100 Hounsfield Unit (HU) value and an added-Gaussian noise with various levels of 5, 10, 25, 50, 75, and 100 HU. The ACR phantom was scanned with a Philips MX-16 slice CT scanner in different slice thicknesses of 1.5, 3, 5, and 7 mm to obtain noise variation. Noise measurement was conducted at the center of the phantom images and four locations close to the edge of the phantom images using different ROI sizes from 3×3 to 41×41 pixels, with an increased size of 2×2 pixels. Results: The use of a minimum ROI size of 21×21 pixels shows noise in the range of ±5% ground truth noise. The measured noise increases above the ±5% range if the used ROI is smaller than 21×21 pixels. Conclusion: A minimum acceptable ROI size is required to maintain the accuracy of noise measurement with a size of 21×21 pixels.

The selection of imaging modality is important in emergency departments because symptoms in children are usually unclear. In children with abdominal pain, differential diagnosis ranges from common diseases to rare entities requiring surgical therapy. Unremarkable findings on routine investigation, such as laboratory tests and plain radiographs, cannot rule out the surgical entities. Close observation and, if needed, further imaging tests are necessary in emergency departments or even in wards for children with recurrent or worsening abdominal pain. We report a rare case of a 13-year-old boy with strangulation of the floating gallbladder by the lesser omentum. This article features the imaging findings of the rare surgical entity.

Introduction The diagnosis of rhinosinusitis is based on clinical grounds having characteristic symptoms, combined with objective evidence of mucosal inflammation. We studied the corelation between the symptoms of the patients, clinical and endoscopic findings with CT scan findings in chronic rhinosinusitis (CRS). Materials and Methods Patients above the age of 15yrs fulfilling the criteria of Chronic sinusitis laid by European position paper on rhinosinusitis and nasal polyps (EPOS) 2012 were prospectively studied. Demographic and clinical profile were noted. Diagnostic Nasal Endoscopy was done and findings were recorded. Patients were undergone CT evaluation after giving appropriate medical management. Clinical, endoscopic and radiological findings were compared with similar studies. Data was analysed using IBM SPSS software version 20. Results This study included 118 patients of Chronic Rhinosinusitis. Patients commonly male between the age group of 21-30 years presented with nasal obstruction, headache and nasal discharge in order of presentation. Diagnostic Nasal endoscopy revealed Septal deviation in 64.4% and medialize uncinate process in 15.2% of cases. Nasal discharge (48.3%) was commonest finding. CT scan suggested deviated nasal septum (70.4%), concha bullosa (30.5%), blocked osteo-meatal complex (68.6%) in patients of CRS. Presence of Agger Nasi cell (49.2%), Haller cell (12.7%) and Onodi cell (15.7%) seen in these patients. Conclusion CT scan and diagnostic endoscopy along with detailed clinical examination are essential component for assessment of a patient with chronic rhinosinusitis. CT scan is considered as gold standard but endoscopy is also a valuable tool for diagnostic evaluation of patients with CRS.

Objective: The aim is to develop a radiomics model based on contrast-enhanced CT scans for preoperative prediction of perirenal fat invasion (PFI) in patients with renal cell carcinoma (RCC). Methods: The CT data of 131 patients with pathology-confirmed PFI status (64 positives) were retrospectively collected and randomly assigned to the training and test datasets. The kidneys and the masses were annotated by semi-automatic segmentation. Eight types of regions of interest (ROI) were chosen for the training of the radiomics models. The areas under the curves (AUCs) from the receiver operating characteristic (ROC) curve analysis were used to analyze the diagnostic performance. Eight types of models with different ROIs have been developed. The models with the highest AUC in the test dataset were used for construction of the corresponding final model, and comparison with radiologists' diagnosis. Results: The AUCs of the models for each ROI was 0.783–0.926, and there was no statistically significant difference between them (P > 0.05). Model 4 was using the ROI of the outer half-ring which extended along the edge of the mass at the outer edge of the kidney into the perirenal fat space with a thickness of 3 mm. It yielded the highest AUC (0.926) and its diagnostic accuracy was higher than the radiologists' diagnosis. Conclusion: We have developed and validated a radiomics model for prediction of PFI on RCC with contrast-enhanced CT scans. The model proved to be more accurate than the radiologists' diagnosis. [ABSTRACT FROM AUTHOR]

Objectives: To validate an artificial intelligence (AI)–based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)–gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. Methods: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. Results: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998–0.999 and 0.989, 95% CI 0.987–0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918–0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748–0.924) on LDCT. Conclusions: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. Key Points: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score–based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT. [ABSTRACT FROM AUTHOR]

Objectives: To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome. Methods: We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center. Results: Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort. Conclusions: Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. Key Points: • Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort. [ABSTRACT FROM AUTHOR]

Objective: To compare the diagnostic performance and inter-reader agreement of the CT-based v2019 versus v2005 Bosniak classification systems for risk stratification of cystic renal lesions (CRL). Methods: This retrospective study included adult patients with CRL identified on CT scan between 2005 and 2018. The reference standard was histopathology or a minimum 4-year imaging follow-up. The studies were reviewed independently by five readers (three senior, two junior), blinded to pathology results and imaging follow-up, who assigned Bosniak categories based on the 2005 and 2019 versions. Diagnostic performance of v2005 and v2019 Bosniak classifications for distinguishing benign from malignant lesions was calculated by dichotomizing CRL into the potential for ablative therapy (III-IV) or conservative management (I-IIF). Inter-reader agreement was calculated using Light's Kappa. Results: One hundred thirty-nine patients with 149 CRL (33 malignant) were included. v2005 and v2019 Bosniak classifications achieved similar diagnostic performance with a sensitivity of 91% vs 91% and a specificity of 89% vs 88%, respectively. Inter-reader agreement for overall Bosniak category assignment was substantial for v2005 (κ = 0.78) and v2019 (κ = 0.75) between senior readers but decreased for v2019 when the Bosniak classification was dichotomized to conservative management (I-IIF) or ablative therapy (III-IV) (0.80 vs 0.71, respectively). For v2019, wall thickness was the morphological feature with the poorest inter-reader agreement (κ = 0.43 and 0.18 for senior and junior readers, respectively). Conclusion: No significant improvement in diagnostic performance and inter-reader agreement was shown between v2005 and v2019. The observed decrease in inter-reader agreement in v2019 when dichotomized according to management strategy may reflect the more stringent morphological criteria. Key Points: • Versions 2005 and 2019 Bosniak classifications achieved similar diagnostic performance, but the specificity of higher risk categories (III and IV) was not increased while one malignant lesion was downgraded to v2019 Bosniak category II (i.e., not subjected to further follow-up). • Inter-reader agreement was similar between v2005 and v2019 but moderately decreased for v2019 when the Bosniak classification was dichotomized according to the potential need for ablative therapies (I-II-IIF vs III-IV). [ABSTRACT FROM AUTHOR]

This technical note describes a novel dual-energy CT (DECT) protocol with iodine map reconstruction that will enable visualization of chronic subdural hematoma (CSDH) membranes. We describe the technique and discuss the potential implications for surgical management. The cohort included 36 patients with 50 hematomas. Enhancing external membrane was demonstrated in all the 50 hematomas, incomplete internal membrane in 13, and complete internal membrane in 23 hematomas. A spandrel sign at the transition zone that indicates partial or complete formation of internal membrane was demonstrated in 36 hematomas. Key Points: • Iodine maps from 5-min delayed post-contrast DECT provide spectral contrast difference and facilitate segregation of chronic subdural hematoma membranes. • The ability to image the membranes helps in assessing the degree of organization of the hematoma by providing the information about the membrane thickness, volume, complexity of the membranes, and the proportion of the liquefied component within the hematoma before surgical procedures are undertaken. • Membrane visualization helps in the localization of the transition zone and extension of the membranes over the cerebral lobes helping in the determination of craniotomy location and size, during membranectomy. [ABSTRACT FROM AUTHOR]

Objectives: To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images. Methods: We developed a quantitative ITH measurement—ITHscore—by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient's prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH. Results: For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC). Conclusions: ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient's prognosis. Key Points: • ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC. • ITHscore is an indicator for tumor phenotypes and patient's prognosis. • ITHscore has the potential to be generalized to other cancer types such as HNC. [ABSTRACT FROM AUTHOR]

Objective: To assess the diagnostic accuracy and agreement of CT and MRI in terms of the Bosniak classification version 2019 (BCv2019). Materials and methods: A prospective multi-institutional study enrolled 63 patients with 67 complicated cystic renal masses (CRMs) discovered during ultrasound examination. All patients underwent CT and MRI scans and histopathology. Three radiologists independently assessed CRMs using BCv2019 and assigned Bosniak class to each CRM using CT and MRI. The final analysis included 60 histopathologically confirmed CRMs (41 were malignant and 19 were benign). Results: Discordance between CT and MRI findings was noticed in 50% (30/60) CRMs when data were analyzed in terms of the Bosniak classes. Of these, 16 (53.3%) were malignant. Based on consensus reviewing, there was no difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT (87.8%; 95% CI = 73.8–95.9% versus 75.6%; 95% CI = 59.7–87.6%; p = 0.09, 84.2%; 95% CI = 60.4–96.6% versus 78.9%; 95% CI = 54.4–93.9%; p = 0.5, and 86.7%; 95% CI = 64.0–86.6% versus 76.7%; 95% CI = 75.4–94.1%; p = 0.1, respectively). The number and thickness of septa and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. The inter-reader agreement (IRA) was substantial for determining the Bosniak class in CT and MRI (k = 0.66; 95% CI = 0.54–0.76, k = 0.62; 95% CI = 0.50–0.73, respectively). The inter-modality agreement of the BCv219 between CT and MRI was moderate (κ = 0.58). Conclusion: In terms of BCv2019, CT and MRI are comparable in the classification of CRMs with no significant difference in diagnostic accuracy and reliability. Key Points: • There is no significant difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT. • The number of septa and their thickness and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. • The inter-reader agreement was substantial for determining the Bosniak class in CT and MRI and the inter-modality agreement of the BCv219 between CT and MRI was moderate. [ABSTRACT FROM AUTHOR]

Objective To explore value of lung air volume ratio on biphasic quantitative CT for diagnosing chronic obstructive pulmonary disease (COPD) in smokers. Methods A total of 140 smokers were divided into COPD (65) and non-COPD (75) groups according to the clinical diagnosis. The clinical data and biphasic quantitative CT in our hospital from June 2019 to June 2021 were prospectively analyzed. The general data included age,smoking index,body mass index (BMI),forced expiratory volume in one second in percent predicted values (FEV1%pred),FEV1/forced vital capacity (FEV1/FVC). Quantitative CT parameters included mean lung density (MLD),lung tissue volume,lung air volume in expiration and inspiration,and the corresponding biphasic ratio. The parameters between the COPD and non-COPD groups were compared using the t test or Mann-Whitney U test. Spearman’s rank correlation coefficient was used to analyze the correlation between quantitative CT indexes and pulmonary function tests. The effect of lung tissue volume ratio and lung air volume ratio on MLD ratio of expiratory to inspiratory phase (MLDex/in) was analyzed by multiple linear regression. Multivariate logistic stepwise regression model was used to screen independent influencing factors of COPD,and the receiver operating characteristic (ROC) curves were drawn to compare the diagnostic probability of logistic regression prediction probability and each independent influencing factor. Results There were significant differences between the two groups in BMI,smoking index,MLDex/in,lung tissue volume ratio and lung air volume ratio (P＜0. 05). MLDex/in (r=-0. 621,-0. 579),lung tissue volume ratio (r=-0. 379,-0. 436) and lung air volume ratio (r=-0. 616,-0. 552) were significantly correlated (all P＜0. 05) with FEV1%pred and FEV1/FVC,respectively. Compared with lung tissue volume ratio (Beta=-0. 311),lung air volume ratio (Beta=1. 054) had a greater influence on the MLDex/inby multiple linear regression (R~2=0. 875,P＜0. 05). Multivariate logistic regression analysis showed that BMI (OR=0. 693,P＜0. 05),smoking index (OR=1. 001,P＜0. 05),and lung air volume ratio (OR=1. 017,P＜0. 05) were independent influencing factors for COPD. The area under the ROC curve of prediction probability value of logistic regression model was 0. 917 with 84. 60%sensitivity and 86. 70%specificity,all of which were better than independent influencing factors. Conclusion Lung air volume ratio is an independent risk factor for COPD in smokers with good diagnostic performance when combined with BMI and smoking index. [ABSTRACT FROM AUTHOR]

Objective To investigate the CT features of polyethylene glycol conjugated asparaginase (PEG-Asp)-related acute pancreatitis (PAP). Methods The clinical information and CT of 10 patients (8 children, median age: 6.5 years; 2 adults, age: 24 and 49 years) with PAP were retrospectively analyzed. The patients received PEG-Asp during chemotherapy for acute lymphoblastic leukemia (7), NK/T-cell lymphoma (2), or acute myeloid leukemia (1). All patients underwent unenhanced (10) and enhanced (7) multi-slice CT scanning. The imaging features were analyzed in correlation with clinical information and treatment history. Results All patients suffered abdominal pain 2-65 days after treatment. The serum amylase or lipase was elevated 3 times above normal in the chemotherapy induction period (5), intensification period (1), consolidation period (1), or maintenance period (1) for leukemia, at the beginning of the first cycle (1) and after the end of the fourth cycle of chemotherapy for NK/T-cell lymphoma. CT showed varying degrees of swelling of the entire pancreas (10), focal density reduction without enhancement (3), peripancreatic fluid collection (7), and blurred fat space around the pancreas (8). There were punctate calcifications in the pancreatic parenchyma (7), patchy hepatic calcifications (6), and calcified gallstones in 2 children. Using the modified CT severity grading system, the acute pancreatitis was severe (2), moderately severe (6), or mild (2). Conclusion PAP is common in patients receiving PEG-Asp-containing regimen for treatment of hematologic diseases. The severity of acute pancreatitis is variable with tendency to develop hepatic and pancreatic calcifications and gallstones. [ABSTRACT FROM AUTHOR]

Objective: Rare disease Background: Pulmonary alveolar microlithiasis (PAM) is an uncommon pulmonary disease characterized by deposition of microliths in the alveoli. In this report, we describe the first ever documented case from the Indonesian population of an adult patient who was diagnosed with PAM based on clinical and pathognomonic radiological findings. Case Report: A 57-year-old man with a 12-year history of progressive shortness of breath on exertion was admitted to our center. When the lungs were listened to, there were coarse crackles and wheezing during inspiration, and the vesicular sound was lower in all thoracic regions. Cardiac auscultation was unremarkable, with fingers having a clubbed drumstick appearance. Bronchoscopy revealed all patent branches of the bronchial tree. Unfortunately, the microliths were absent, and the histology findings from bronchoalveolar lavage and transbronchial lung biopsy were inconclusive. Radiologic features of a chest radiograph show the characteristic finding of multiple diffuse micronodules with a high density in both lungs. A high-resolution computed tomography (HRCT) scan corroborated the typical findings of extensive intraparenchymal calcified micronodules with diffuse groundglass attenuation areas. Black pleural line signs were also seen. Conclusions: PAM is a rare disease with a chronic clinical course and varying manifestations according to phase, but progressive deterioration may result in a poor prognosis. It is particularly important for clinicians to be able to narrow down the differential diagnosis of multiple diffuse micronodules of the lungs. When a non-invasive method of diagnosis is preferred, chest X-rays and, even better, HRCT should be used to find the characteristic features of alveolar microlithiasis. [ABSTRACT FROM AUTHOR]

Little information is available about how intravenous bolus injection of iopromide 370 twice in a short time will affect hemodynamics and whether the changes reach clinically relevant levels. In the present study, 31 healthy adult volunteers received abdominal contrast-enhanced CT and coronary CTA sequential examinations. The same dose and rate of normal saline was injected 30 min in advance as self-control. Hemodynamic data were noninvasively collected at selected time points from 1 min prior to injection to 30 min post-injection. The results showed that after iopromide 370 injection, except for stroke volume, all other indicators changed immediately during the first injection, changed most significantly during the second injection (P < 0.05), and returned to baseline within 10 min. Heart rate and cardiac output exhibited the most pronounced changes, with an increasing rate of 33.5% and 33.8%, respectively. For indicators with a change range of > 15% during the second injection, except for mean arterial pressure and total peripheral resistance, the proportions of subjects for the other indicators between the two groups were statistically different (P < 0.05). In conclusion, intravenous bolus injection of iopromide 370 twice in dual-site sequential examinations induced dose-cumulative and time-dependent hemodynamic effects, which all fluctuated within the normal ranges. [ABSTRACT FROM AUTHOR]

Objective: The radiological and functional lung sequelae in COVID-19 survivors remain unclear. We compared the chest computed tomography findings of COVID-19 patients with normal and abnormal pulmonary function test results in the post-recovery phase. Methods: The data of consecutive patients who underwent pulmonary function tests and chest computed tomography within 14 days after recovery from COVID-19 at two medical centers between May and October 2020 were collected retrospectively. Two thoracic radiologists who were blinded to the clinical information and pulmonary function test results classified the patients according to the computed tomography features, evidence of fibrotic-like changes, and semiquantitative quantification of the extent of pulmonary abnormalities. The clinical characteristics and computed tomography findings of patients with normal pulmonary function test results were compared with those of patients with abnormal results. Results: A total of 101 COVID-19 survivors, comprising 48 ambulatory and 53 hospitalized patients, were included at a median of 95 days from initial symptom onset. Computed tomography revealed fibrotic-like changes in 10.9% of patients. A reduction in the diffusion capacity of carbon monoxide was the most common lung function abnormality (19.8%). Abnormal diffusion capacity of carbon monoxide was associated with the presence and extension of lung opacities on chest computed tomography scans and fibrotic pulmonary abnormalities. The sensitivity, specificity, and accuracy of reduced diffusion capacity of carbon monoxide for detecting fibrotic-like pulmonary changes on chest computed tomography scans were 72.7%, 87.8%, and 86.1%, respectively. Conclusion: Our study suggests that the presence of an abnormal diffusion capacity of carbon monoxide in the post-recovery phase of COVID-19 is associated with a greater risk of longterm parenchymal lung disease, as evidenced by the presence of fibrotic-like changes on chest computed tomography scans, such as traction bronchiectasis and architectural distortion. [ABSTRACT FROM AUTHOR]