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Background CT-based body composition measures derived from fully automated artificial intelligence tools are promising for opportunistic screening. However, body composition thresholds associated with adverse clinical outcomes are lacking. Purpose To determine population and sex-specific thresholds for muscle, abdominal fat, and abdominal aortic calcium measures at abdominal CT for predicting risk of death, adverse cardiovascular events, and fragility fractures. Materials and Methods In this retrospective single-center study, fully automated algorithms for quantifying skeletal muscle (L3 level), abdominal fat (L3 level), and abdominal aortic calcium were applied to noncontrast abdominal CT scans from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up documented subsequent death, adverse cardiovascular events (myocardial infarction, cerebrovascular event, and heart failure), and fragility fractures. Receiver operating characteristic (ROC) curve analysis was performed to derive thresholds for body composition measures to achieve optimal ROC curve performance and high specificity (90%) for 10-year risks. Results A total of 9223 asymptomatic adults (mean age, 57 years ± 7 [SD]; 5152 women and 4071 men) were evaluated (median follow-up, 9 years). Muscle attenuation and aortic calcium had the highest diagnostic performance for predicting death, with areas under the ROC curve of 0.76 for men (95% CI: 0.72, 0.79) and 0.72 for women (95% CI: 0.69, 0.76) for muscle attenuation. Sex-specific thresholds were higher in men than women ( P < .001 for muscle attenuation for all outcomes). The highest-performing markers for risk of death were muscle attenuation in men (31 HU; 71% sensitivity [164 of 232 patients]; 72% specificity [1114 of 1543 patients]) and aortic calcium in women (Agatston score, 167; 70% sensitivity [152 of 218 patients]; 70% specificity [1427 of 2034 patients]). Ninety-percent specificity thresholds for muscle attenuation for both risk of death and fragility fractures were 23 HU (men) and 13 HU (women). For aortic calcium and risk of death and adverse cardiovascular events, 90% specificity Agatston score thresholds were 1475 (men) and 735 (women). Conclusion Sex-specific thresholds for automated abdominal CT-based body composition measures can be used to predict risk of death, adverse cardiovascular events, and fragility fractures. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ohliger in this issue.

Adenomyomatosis and cholesterolosis of the gallbladder, collectively termed hyperplastic cholecystosis, are commonly encountered incidental findings on imaging studies performed for a variety of indications including biliary colic or nonspecific abdominal pain. These pathologies are rarely the source of symptoms, generally considered benign and do not require further work-up. However, their imaging characteristics can overlap with more sinister conditions that should not be missed. In this review, the imaging findings of adenomyomatosis and cholesterolosis will be reviewed followed by other gallbladder pathologies that might mimic these conditions radiologically. Important differentiating factors will be discussed that can aid the radiologist in making a more confident imaging diagnosis., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Neuroendocrine neoplasms of the small bowel are a diverse group of tumors with a broad spectrum of imaging findings and clinical implications. Most tumors originate in close proximity to the ileocecal valve and most commonly metastasize to the mesentery and liver. This review will highlight the imaging findings of primary and metastatic small bowel neuroendocrine neoplasm that are most relevant to the surgical team., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To review existing structured MRI reports for primary staging of rectal cancer and create a new, freely available structured report based on multidisciplinary expert opinion and literature review., Methods: Twenty abdominal imaging experts from the Society of Abdominal Radiology (SAR)'s Disease Focused Panel (DFP) on Rectal and Anal Cancer completed a questionnaire and participated in a subsequent consensus meeting based on the RAND-UCLA Appropriateness Method. Twenty-two items were classified via a group survey as "appropriate" or "inappropriate" (defined by ≥ 70% consensus), or "needs group discussion" (defined by < 70% consensus). Certain items were also discussed with multidisciplinary team members from colorectal surgery, oncology and pathology., Results: After completion of the questionnaire, 16 (72%) items required further discussion (< 70% consensus). Following group discussion, consensus was achieved for 21 (95%) of the items. Based on the consensus meeting, a revised structured report was developed. The most significant modifications included (1) Exclusion of the T2/early T3 category; (2) Replacement of the term "circumferential resection margin (CRM)" with "mesorectal fascia (MRF)"; (3) A revised definition of "mucinous content"; (4) Creation of two distinct categories for suspicious lymph nodes (LNs) and tumor deposits; and (5) Classification of suspicious extra-mesorectal LNs by anatomic location., Conclusion: The SAR DFP on Rectal and Anal Cancer recommends using this newly updated reporting template for primary MRI staging of rectal cancer., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT examinations for staging, treatment response, and surveillance, providing the opportunity for quantitative body composition assessment to be performed as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semiautomated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.

An automated pipeline is developed for the serial assessment of renal calculi using computed tomography (CT) scans obtained at multiple time points. This retrospective study included 722 scans from 330 patients chosen from 8544 asymptomatic patients who underwent two or more CTC (CT colonography) or non-enhanced abdominal CT scans between 2004 and 2016 at a single medical center. A pre-trained deep learning (DL) model was used to segment the kidneys and the calculi on the CT scans at each time point. Based on the output of the DL, 330 patients were identified as having a stone candidate on at least one time point. Then, for every patient in this group, the kidneys from different time points were registered to each other, and the calculi present at multiple time points were matched to each other using proximity on the registered scans. The automated pipeline was validated by having a blinded radiologist assess the changes manually. New graph-based metrics are introduced in order to evaluate the performance of our pipeline. Our method shows high fidelity in tracking changes in renal calculi over multiple time points.

Purpose: To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard., Materials and Methods: This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed., Results: The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%)., Conclusion: The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment. Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: P.J.P. Consulting fees for advisor to Bracco, Nanox, and GE Healthcare; stock/stock options in Nanox. T.N. No relevant relationships. A.A.P. Support for attending meetings and/or travel from UW Health for academic radiology conferences. P.M.G. No relevant relationships. S.J. No relevant relationships. R.M.S. Grant from PingAn (CRADA); royalties for patents or licenses from iCAD, Philips, ScanMed, PingAn, Translation Holdings; associate editor of Radiology: Artificial Intelligence. J.W.G. R01 LM013151/LM/NLM NIH HHS/United States grant., (© 2022 by the Radiological Society of North America, Inc.)

Purpose: To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis., Materials and Methods: For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0-6, 2001-2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2., Results: Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44-56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41-57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, P < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; P < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively., Conclusion: The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis. Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: S.L. No relevant relationships. D.C.E. No relevant relationships. A.H.Y. No relevant relationships. C.K. No relevant relationships. D.E.K. No relevant relationships. M.G.L. Prior grant funding from Philips, Ethicon. P.J.P. Consulting fees from Bracco; stock/stock options in SHINE and Elucent; royalties from Elsevier. R.M.S. Royalties for patent and software licenses (iCAD, PingAn, Philips, ScanMed, Translation Holdings); PingAn has cooperative research and development agreement with author's institution; associate editor of Radiology: Artificial Intelligence., (© 2022 by the Radiological Society of North America, Inc.)

Percutaneous ultrasound-guided biopsies have become the standard of practice for tissue diagnosis in the abdomen and pelvis for many sites including liver, kidney, abdominal wall, and peripheral nodal stations. Additional targets may appear difficult or impossible to safely biopsy by ultrasound due to interposed bowel loops/vasculature, deep positioning, association with the bowel, or concern for poor visibility; however, by optimizing technique, it is often possible to safely and efficiently use real-time ultrasound guidance for sampling targets that normally would be considered only appropriate for CT guided or surgical/endoscopic biopsy., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Purpose: To evaluate the efficacy of percutaneous biopsy for diagnosing intrahepatic cholangiocarcinoma (IHCCA)., Methods: Retrospective review of biopsy and pathology databases from 2006 to 2019 yielded 112 patients (54F/58 M; mean age, 62.9 years; 27 cirrhotic) with IHCCA who underwent percutaneous biopsy. Data regarding the lesion, biopsy procedure technique, and diagnostic yield were collected. If biopsy was non-diagnostic or discordant with imaging, details of repeat biopsy or resection/explant were gathered. A control group of 100 consecutive patients (56F/44 M; mean age, 63 years, 5 cirrhotic) with focal liver lesions > 1 cm was similarly assessed., Results: Mean IHCCA lesion size was 6.1 ± 3.6 cm, with dominant lesion sampled in 78% (vs. satellite in 22%). 95% (n = 106) were US guided and 96% were core biopsies (n = 108), typically 18G (n = 102, 91%), median 2 passes. 18 patients (16%) had discordant/ambiguous pathology results requiring repeat biopsy, with two patients requiring 3-4 total attempts. A 4.4% minor complication rate was seen. Mean time from initial biopsy to final diagnosis was 60 ± 120 days. Control group had mean lesion size of 2.9 ± 2.5 cm and showed a non-diagnostic rate of 3.3%, both significantly lower than that seen with CCA, with average time to diagnosis of 21 ± 28.8 days (p = 0.002, p = 0.001)., Conclusion: IHCCA is associated with lower diagnostic yield at initial percutaneous biopsy, despite larger target lesion size. If a suspicious lesion yields a biopsy result discordant with imaging, the radiologist should recommend prompt repeat biopsy to prevent delay in diagnosis., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: Evaluate the impact of positive oral contrast material (POCM) for non-traumatic abdominal pain on diagnostic confidence, diagnostic rate, and ED throughput., Materials and Methods: ED oral contrast guidelines were changed to limit use of POCM. A total of 2,690 abdominopelvic CT exams performed for non-traumatic abdominal pain were prospectively evaluated for diagnostic confidence (5-point scale at 20% increments; 5 = 80-100% confidence) during a 24-month period. Impact on ED metrics including time from CT order to exam, preliminary read, ED length of stay (LOS), and repeat CT scan within 7 days was assessed. A subset of cases (n = 729) was evaluated for diagnostic rate. Data were collected at 2 time points, 6 and 24 months following the change., Results: A total of 38 reviewers were participated (28 trainees, 10 staff). 1238 exams (46%) were done with POCM, 1452 (54%) were performed without POCM. For examinations with POCM, 80% of exams received a diagnostic confidence score of 5 (mean, 4.78 ± 0.43; 99% ≥ 4), whereas 60% of exams without POCM received a score of 5 (mean, 4.51 ± 0.70; 92% ≥ 4; p < .001). Trainees scored 1,523 exams (57%, 722 + POCM, 801 -POCM) and showed even lower diagnostic confidence in cases without PCOM compared with faculty (mean, 4.43 ± 0.68 vs. 4.59 ± 0.71; p < 0.001). Diagnostic rate in a randomly selected subset of exams (n = 729) was 54.2% in the POCM group versus 56.1% without POCM (p < 0.655). CT order to exam time decreased by 31 min, order to preliminary read decreased by 33 min, and ED LOS decreased by 30 min (approximately 8% of total LOS) in the group without POCM compared to those with POCM (p < 0.001 for all). 205 patients had a repeat scan within 7 days, 74 (36%) had IV contrast only, 131 (64%) had both IV and oral contrast on initial exam. Findings were consistent both over a 6-month evaluation period as well as the full 24-month study period., Conclusion: Limiting use of POCM in the ED for non-traumatic abdominal pain improved ED throughput but impaired diagnostic confidence, particularly in trainees; however, it did not significantly impact diagnostic rates nor proportion of repeat CT exams., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Extraskeletal Ewing sarcoma (EES) is a rare subtype in the Ewing sarcoma family of tumors (ESFT), which also includes Ewing sarcoma of bone (ESB) and, more recently, primitive neuroectodermal tumors. Although these tumors often have different manifestations, they are grouped on the basis of common genetic translocation and diagnosis from specific molecular and immunohistochemical features. While the large majority of ESFT cases occur in children and in bones, approximately 25% originate outside the skeleton as EES. Importantly, in the adult population these extraskeletal tumors are more common than ESB. Imaging findings of EES tumors are generally nonspecific, with some variation based on location and the tissues involved. A large tumor with central necrosis that does not cross the midline is typical. Despite often nonspecific findings, imaging plays an important role in the evaluation and management of ESFT, with MRI frequently the preferred imaging modality for primary tumor assessment and local staging. Chest CT and fluorine 18 fluorodeoxyglucose PET/CT are most sensitive for detecting lung and other distant or nodal metastases. Management often involves chemotherapy with local surgical excision, when possible. A multidisciplinary treatment approach should be used given the propensity for large tumor size and local invasion, which can make resection difficult. Despite limited data, outcomes are similar to those of other ESFT cases, with 5-year survival exceeding 80%. However, with metastatic disease, the long-term prognosis is poor. © RSNA, 2022.

Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this topic have shown significant results but were limited by manual methods and small study samples. Purpose To investigate abdominal CT biomarkers for type 2 diabetes mellitus in a large clinical data set using fully automated deep learning. Materials and Methods For external validation, noncontrast abdominal CT images were retrospectively collected from consecutive patients who underwent routine colorectal cancer screening with CT colonography from 2004 to 2016. The pancreas was segmented using a deep learning method that outputs measurements of interest, including CT attenuation, volume, fat content, and pancreas fractal dimension. Additional biomarkers assessed included visceral fat, atherosclerotic plaque, liver and muscle CT attenuation, and muscle volume. Univariable and multivariable analyses were performed, separating patients into groups based on time between type 2 diabetes diagnosis and CT date and including clinical factors such as sex, age, body mass index (BMI), BMI greater than 30 kg/m 2 , and height. The best set of predictors for type 2 diabetes were determined using multinomial logistic regression. Results A total of 8992 patients (mean age, 57 years ± 8 [SD]; 5009 women) were evaluated in the test set, of whom 572 had type 2 diabetes mellitus. The deep learning model had a mean Dice similarity coefficient for the pancreas of 0.69 ± 0.17, similar to the interobserver Dice similarity coefficient of 0.69 ± 0.09 ( P = .92). The univariable analysis showed that patients with diabetes had, on average, lower pancreatic CT attenuation (mean, 18.74 HU ± 16.54 vs 29.99 HU ± 13.41; P < .0001) and greater visceral fat volume (mean, 235.0 mL ± 108.6 vs 130.9 mL ± 96.3; P < .0001) than those without diabetes. Patients with diabetes also showed a progressive decrease in pancreatic attenuation with greater duration of disease. The final multivariable model showed pairwise areas under the receiver operating characteristic curve (AUCs) of 0.81 and 0.85 between patients without and patients with diabetes who were diagnosed 0-2499 days before and after undergoing CT, respectively. In the multivariable analysis, adding clinical data did not improve upon CT-based AUC performance (AUC = 0.67 for the CT-only model vs 0.68 for the CT and clinical model). The best predictors of type 2 diabetes mellitus included intrapancreatic fat percentage, pancreatic fractal dimension, plaque severity between the L1 and L4 vertebra levels, average liver CT attenuation, and BMI. Conclusion The diagnosis of type 2 diabetes mellitus was associated with abdominal CT biomarkers, especially measures of pancreatic CT attenuation and visceral fat. © RSNA, 2022 Online supplemental material is available for this article.

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors. Two widely used differential operators, i.e., the gradient operator and Hessian operator, are utilized to generate the first and second order derivative images. These derivative volumetric images are used to produce two angle-based and two vector-based (including both angle and magnitude) textures. Next, a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications. To evaluate the performance of our method, experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography. We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13% evaluated by the area under the receiver operating characteristics curves., (© 2022. The Author(s).)

Objective: To determine the clinical impact of the second examination when both CT and TVUS are obtained in the same ED visit for acute pelvic/lower abdominal symptoms in non-pregnant premenopausal women., Methods: 200 consecutive non-pregnant premenopausal women (mean age, 31.8 years; range, 18-49 years) who underwent both ED-based TVUS and abdominopelvic CT evaluation for acute symptoms over a 12 month period were included; 107 women had TVUS first, followed by CT; 93 women had CT first. All relevant clinical, radiologic, and pathologic findings were reviewed to establish a final diagnosis. Any additional clinical impact provided by the second imaging test was assessed by two experienced abdominal radiologists., Results: Initial TVUS was interpreted as normal (n = 63) or mentioned incidental findings (n = 11) in 69% (74/107); subsequent CT established a non-gynecologic GI/GU etiology in 25 (34%). For 37% (34/93) of CT exams interpreted as normal, TVUS added no new information. In 32 cases (34%), TVUS further excluded ovarian torsion/adnexal pathology when initial CT was indeterminate/equivocal. Overall, CT following TVUS provided a key new or alternative diagnosis in 26% (28/107), whereas TVUS after CT provided a relevant new/alternative diagnosis in only 1/93 cases (p < 0.001). In nine cases (8%), CT confirmed a positive US diagnosis but detected relevant additional diagnostic information., Conclusion: CT following negative TVUS frequently identified a non-gynecologic cause of acute pelvic or lower abdominal symptoms in non-pregnant premenopausal women, whereas the main benefit of TVUS after CT was more confident exclusion of ovarian torsion., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

BACKGROUND. In single-institution multireader studies, the liver surface nodularity (LSN) score accurately detects advanced liver fibrosis and cirrhosis and predicts liver decompensation in patients with chronic liver disease (CLD) from hepatitis C virus (HCV). OBJECTIVE. The purpose of this study was to assess the diagnostic performance of the LSN score alone and in combination with the (FIB-4; fibrosis index based on four factors) to detect advanced fibrosis and cirrhosis and to predict future liver-related events in a multiinstitutional cohort of patients with CLD from HCV. METHODS. This retrospective study included 40 consecutive patients, from each of five academic medical centers, with CLD from HCV who underwent nontargeted liver biopsy within 6 months before or after abdominal CT. Clinical data were recorded in a secure web-based database. A single central reader measured LSN scores using software. Diagnostic performance for detecting liver fibrosis stage was determined. Multivariable models were constructed to predict baseline liver decompensation and future liver-related events. RESULTS. After exclusions, the study included 191 patients (67 women, 124 men; mean age, 54 years) with fibrosis stages of F0-F1 ( n = 37), F2 ( n = 44), F3 ( n = 46), and F4 ( n = 64). Mean LSN score increased with higher stages (F0-F1, 2.26 ± 0.44; F2, 2.35 ± 0.37; F3, 2.42 ± 0.38; F4, 3.19 ± 0.89; p < .001). The AUC of LSN score alone was 0.87 for detecting advanced fibrosis (≥ F3) and 0.89 for detecting cirrhosis (F4), increasing to 0.92 and 0.94, respectively, when combined with FIB-4 scores (both p = .005). Combined scores at optimal cutoff points yielded sensitivity of 75% and specificity of 82% for advanced fibrosis, and sensitivity of 84% and specificity of 85% for cirrhosis. In multivariable models, LSN score was the strongest predictor of baseline liver decompensation (odds ratio, 14.28 per 1-unit increase; p < .001) and future liver-related events (hazard ratio, 2.87 per 1-unit increase; p = .03). CONCLUSION. In a multiinstitutional cohort of patients with CLD from HCV, LSN score alone and in combination with FIB-4 score exhibited strong diagnostic performance in detecting advanced fibrosis and cirrhosis. LSN score also predicted future liver-related events. CLINICAL IMPACT. The LSN score warrants a role in clinical practice as a quantitative marker for detecting advanced liver fibrosis, compensated cirrhosis, and decompensated cirrhosis and for predicting future liver-related events in patients with CLD from HCV.

BACKGROUND. Calibrated CT fat fraction (FF CT ) measurements derived from un-enhanced abdominal CT reliably reflect liver fat content, allowing large-scale population-level investigations of steatosis prevalence and associations. OBJECTIVE. The purpose of this study was to compare the prevalence of hepatic steatosis, as assessed by calibrated CT measurements, between population-based Chinese and U.S. cohorts, and to investigate in these populations the relationship of steatosis with age, sex, and body mass index (BMI). METHODS. This retrospective study included 3176 adults (1985 women and 1191 men) from seven Chinese provinces and 8748 adults (4834 women and 3914 men) from a single U.S. medical center, all drawn from previous studies. All participants were at least 40 years old and had undergone unenhanced abdominal CT in previous studies. Liver fat content measurements on CT were cross-calibrated to MRI proton density fat fraction measurements using phantoms and expressed as adjusted FF CT measurements. Mild, moderate, and severe steatosis were defined as adjusted FF CT of 5.0-14.9%, 15.0-24.9%, and 25.0% or more, respectively. The two cohorts were compared. RESULTS. In the Chinese and U.S. cohorts, the median adjusted FF CT for women was 4.7% and 4.8%, respectively, and that for men was 5.8% and 6.2%, respectively. In the Chinese and U.S. cohorts, steatosis prevalence for women was 46.3% and 48.7%, respectively, whereas that for men was 58.9% and 61.9%, respectively. Severe steatosis prevalence was 0.9% and 1.8% for women and 0.2% and 2.6% for men in the Chinese and U.S. cohorts, respectively. Adjusted FF CT did not vary across age decades among women or men in the Chinese cohort, although it increased across age decades among women and men in the U.S. cohort. Adjusted FF CT and BMI exhibited weak correlation ( r = 0.312-0.431). Among participants with normal BMI, 36.8% and 38.5% of those in the Chinese and U.S. cohorts, respectively, had mild steatosis, and 3.0% and 1.5% of those in the Chinese and U.S. cohorts, respectively, had moderate or severe steatosis. Among U.S. participants with a BMI of 40.0 or greater, 17.7% had normal liver content. CONCLUSION. Steatosis and severe steatosis had higher prevalence in the U.S. cohort than in the Chinese cohort in both women and men. BMI did not reliably predict steatosis. CLINICAL IMPACT. The findings provide new information on the dependence of hepatic steatosis on age, sex, and BMI.

BACKGROUND. The serrated pathway for colorectal cancer (CRC) development is increasingly recognized. Sessile serrated lesions (SSLs) that are large (≥ 10 mm) and/or have dysplasia (i.e., high-risk SSLs) are at higher risk of progression to CRC. Detection of SSLs is challenging given their predominantly flat and right-sided location. The yield of noninvasive screening tests for detection of high-risk SSLs is unclear. OBJECTIVE. The aim of this study was to compare noninvasive screening detection of high-risk SSLs between the multitarget stool DNA (mt-sDNA) test and CT colonography (CTC). METHODS. This retrospective study included 7974 asymptomatic adults (4705 women, 3269 men; mean age, 60.0 years) who underwent CRC screening at a single center by mt-sDNA from 2014 to 2019 ( n = 3987) or by CTC from 2009 to 2019 ( n = 3987). Clinical interpretations of CTC examinations were recorded. Subsequent colonoscopy findings and histology of resected polyps were also recorded. Chi-square or two-sample t tests were used to compare results between mt-sDNA and CTC using 6-mm and 10-mm thresholds for test positivity. RESULTS. The overall colonoscopy referral rate for a positive screening test was 13.1% (522/3987) for mt-sDNA versus 12.2% (487/3987; p = .23) and 6.5% (260/3987; p < .001) for CTC at 6-mm and 10-mm thresholds, respectively. The PPV for high-risk SSLs was 5.5% (26/476) for mt-sDNA versus 14.4% (66/457; p < .001) and 25.9% (63/243; p < .001) for CTC at the 6-mm and 10-mm thresholds, respectively. The overall screening yield of high-risk SSLs was 0.7% (26/3987) for mt-sDNA versus 1.7% (66/3987; p < .001) and 1.6% (63/3987; p < .001) for CTC at 6-mm and 10-mm thresholds, respectively. CONCLUSION. CTC at 6-mm and 10-mm thresholds had significantly higher yield and PPV for high-risk SSLs compared with mt-sDNA. CLINICAL IMPACT. The significantly higher detection of high-risk SSLs by CTC than by mt-sDNA should be included in discussions with patients who decline colonoscopy and opt for noninvasive screening.

Purpose: Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning-based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low-dose CT or tested on a large-scale external dataset., Methods: We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient-level labels were used as an external validation set. A 3D U-Net model was employed to segment the kidneys, followed by gradient-based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive regions., Results: The system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low-dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements ( r 2 = 0.95 $r^2 = 0.95$ ). For patient-level classification, the system achieved an area under the receiver-operating characteristic of 0.95 on an external validation set (sensitivity = 0.88, specificity = 0.91 at the Youden point). A common cause of false positives were small atherosclerotic plaques in the renal sinus that simulated kidney stones., Conclusions: Our deep-learning-based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set., (© 2022 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Objective: To develop and validate a radiomic nomogram for individualized prediction of hepatocellular carcinoma (HCC) in HBV cirrhosis patients based on baseline magnetic resonance imaging examinations and clinical data., Methods: 364 patients with HBV cirrhosis from five hospitals were assigned to the training, internal validation, external validation-1 or external validation-2 cohort. All patients underwent baseline magnetic resonance image (MRI) scans and clinical follow-up within three-year time. Clinical risk factors and MRI-based features were extracted and analyzed. The radiomic signatures were built using the radiomics-score (Rad-score) that calculated for each patient as a linear weighted combination of selected MRI-based features. Prognostic performances of the clinical and radiomic nomograms were evaluated with Cox modeling in the training and validation cohorts., Results: Eighteen features were selected for inclusion in the Rad-score prognostic model. The radiomic signature from multi-sequence MRI yielded a concordance index (C-index) of 0.710, 0.681, 0.632 and 0.658 in the training, internal validation, external validation-1, external validation-2 cohorts, respectively. Sex and Child-Turcotte-Pugh (CTP) class were the most prognostic clinical risk factors in univariate Cox proportional hazards analyses. The radiomic combined nomogram that integrated the radiomic signature with the clinical factors yielded a C-index of 0.746, 0.710, and 0.641 in the training, internal validation, and external validation-1 cohorts, respectively, which was an improvement over either the clinical nomogram or radiomic signature alone., Conclusion: We developed an MRI-based radiomic combined nomogram with good discrimination ability for the individualized prediction of HCC in HBV cirrhosis patients within three-year time., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wei, Gong, He, Liu, Liu, Yang, Zhou, Liang, Zhan, Xia, Duan, Lin, Han, Li, Qin, Pickhardt and Deng.)

Purpose: Simple renal cysts are common benign lesions that arise from the renal parenchyma. Cyst growth can lead to confusion as well as concern from patients and referring providers about the need for imaging follow-up or additional evaluation. The purpose of this study was to evaluate the natural history of simple renal cysts and determine the best metric to characterize cyst evolution., Methods: 222 simple renal cysts in 182 adults (age = 58.4 ± 6.0 years) were longitudinally evaluated on non-contrast CT examinations over a mean interval of 7.5 ± 2.8 years. Axial long axis, surface area, and volume were evaluated at baseline and follow-up CT examinations. Absolute and percent annualized growth rates were computed between CT studies for each parameter., Results: At baseline CT examinations, mean (± SD) axial long axis, surface area, and volume measurements were 2.5 ± 1.7 cm, 2.5 ± 4.5 cm 2 , and 17.6 ± 52.5 ml, respectively. On follow-up examinations, measurements were 3.4 ± 2.0 cm, 4.2 ± 5.9 cm 2 , and 34.4 ± 92.3 ml, respectively. Significant differences (p < 0.01) were found between baseline and follow-up values for each parameter. The absolute growth rate of each parameter was + 0.1 ± 0.1 cm/year, + 2.1 ± 3.4 cm 2 /year, and + 2.0 ± 5.6 ml/year, respectively. The percent annualized growth rate for each parameter was +6.5 ± 7.3%/year, +18 ± 24%/year, and +46 ± 100%/year, respectively. Overall, 86% (190/222) of cysts increased in size over time; most notably 78% (174/222) increased by ≥ 6% in volume per year. None of the simple cysts developed septations or solid components on follow-up examinations., Conclusion: The majority of simple renal cysts increase in size over time, which was not associated with the development of complex features. Surface area and volume are the parameters most indicative of cyst growth or regression over time. In patients with enlarging asymptomatic simple renal cysts, no follow-up imaging is indicated., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Traditionally, atherosclerotic risk factors for cardiovascular disease and cancer are assessed using coronary artery calcium scoring. However, this neglects the impact of atherosclerotic disease more proximal to the cancer site. This study assesses whether aortoiliac atherosclerotic plaque is associated with prostate cancer. The dataset consisted of abdominopelvic CT of 93 patients with prostate cancer and 186 asymptomatic patients who underwent CT colonography as an age- and gender-matched control group. Agatston scores were measured in the abdominal aorta, common iliac, and internal iliac arteries. The scores were evaluated for associations with age, Framingham risk score, and prostate cancer-related biomarkers, including prostate-specific antigen, Gleason score, tumor location, prostatectomy, androgen deprivation therapy, mortality, and bone metastasis. The atherosclerotic plaque of prostate cancer patients did not differ from the control group (p = 0.22) and was not correlated with any of the prostate cancer-related biomarkers (p > 0.05). However, Agatston scores of abdominal plaques correlated well with age (p < 0.001) and Framingham risk scores (p = 0.002).

Background: As the US population ages, cancer incidence and prevalence are projected to increase. In the last decade, there has been an increased interest in the opportunistic use of computed tomography (CT) scan data to predict cancer prognosis and inform treatment based on body composition measures, especially muscle measures for sarcopenia., Objective: This article aimed to perform a systematic review of current literature related to CT assessment of muscle attenuation values for myosteatosis in colorectal cancer (CRC) survival prediction., Results: Initial broad search of CT and CRC yielded 4234 results. A more focused search strategy narrowed this to 129 research papers, and 13 articles met the final inclusion criteria. Twelve of 13 studies found a statistically significant decrease in overall survival according to Hounsfield unit (HU)-based sarcopenia, with hazard ratios ranging from 1.36 to 2.94 (mean, 1.78). However, the specific criteria used to define myosteatosis by CT varied widely, with attenuation thresholds ranging from 22.5 to 47.3 HU, often further subdivided by sex and/or body mass index., Conclusions: Current evidence suggests that a strong association between CT-based muscle attenuation values for myosteatosis assessment correlates with overall survival in CRC. However, more research is needed to verify these findings and determine appropriate threshold values for more diverse patient populations. Because CRC patients are staged and followed by CT, the opportunity exists for routine objective myosteatosis assessment in the clinical setting., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

With optimized technique, the water-soluble contrast challenge is effective at triaging patients for operative vs non-operative management of suspected small bowel obstruction. Standardized study structure and interpretation guidelines aid in clinical efficacy and ease of use. Many tips and tricks exist regarding technique and interpretation, and their understanding may assist the interpreting radiologist. In the future, a CT-based water-soluble contrast challenge, utilizing oral contrast given as part of the initial CT examination, might allow for a more streamlined algorithm and provide more rapid results.

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening ( n = 1960) or renal donor evaluation ( n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problems, this work proposes an adaptive learning model to integrate multi-scale texture features. Methods: To mitigate feature variation, the whole feature set is geometrically split into several independent subsets that are ranked by a learning evaluation measure after preliminary classifications. To reduce feature redundancy, a bottom-up hierarchical learning framework is proposed to ensure monotonic increase of classification performance while integrating these ranked sets selectively. Two types of classifiers, traditional (random forest + support vector machine)- and convolutional neural network (CNN)-based, are employed to perform the polyp classification under the proposed framework with extended Haralick measures and gray-level co-occurrence matrix (GLCM) as inputs, respectively. Experimental results are based on a retrospective dataset of 63 polyp masses (defined as greater than 3 cm in largest diameter), including 32 adenocarcinomas and 31 benign adenomas, from adult patients undergoing first-time computed tomography colonography and who had corresponding histopathology of the detected masses. Results: We evaluate the performance of the proposed models by the area under the curve (AUC) of the receiver operating characteristic curve. The proposed models show encouraging performances of an AUC score of 0.925 with the traditional classification method and an AUC score of 0.902 with CNN. The proposed adaptive learning framework significantly outperforms nine well-established classification methods, including six traditional methods and three deep learning ones with a large margin. Conclusions: The proposed adaptive learning model can combat the challenges of feature variation through a multiscale grouping of feature inputs, and the feature redundancy through a hierarchal sorting of these feature groups. The improved classification performance against comparative models demonstrated the feasibility and utility of this adaptive learning procedure for feature integration.

BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. OBJECTIVE. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. METHODS. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. RESULTS. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1-11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture ( p = .18-.98) or death ( p = .19-.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). CONCLUSION. Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. CLINICAL IMPACT. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.

Objectives: To compare the diagnostic accuracy of generalist radiologists working in a community setting against abdominal radiologists working in an academic setting for the interpretation of MR when diagnosing acute appendicitis among emergency department patients., Methods: This observational study examined MR image interpretation (non-contrast MR with diffusion-weighted imaging and intravenous contrast-enhanced MR) from a prospectively enrolled cohort at an academic hospital over 18 months. Eligible patients had an abdominopelvic CT ordered to evaluate for appendicitis and were > 11 years old. The reference standard was a combination of surgery and pathology results, phone follow-up, and chart review. Six radiologists blinded to clinical information, three each from community and academic practices, independently interpreted MR and CT images in random order. We calculated test characteristics for both individual and group (consensus) diagnostic accuracy then performed Chi-square tests to identify any differences between the subgroups., Results: Analysis included 198 patients (114 women) with a mean age of 31.6 years and an appendicitis prevalence of 32.3%. For generalist radiologists, the sensitivity and specificity (95% confidence interval) were 93.8% (84.6-98.0%) and 88.8% (82.2-93.2%) for MR and 96.9% (88.7-99.8%) and 91.8% (85.8-95.5%) for CT. For fellowship-trained radiologists, the sensitivity and specificity were 96.9% (88.2-99.5%) and 89.6% (82.8-94%) for MR and 98.4% (90.5-99.9%) and 93.3% (87.3-96.7%) for CT. No statistically significant differences were detected between radiologist groups (p = 1.0, p = 0.53, respectively) or when comparing MR to CT (p = 0.21, p = 0.17, respectively)., Conclusions: MR is a reliable, radiation-free imaging alternative to CT for the evaluation of appendicitis in community-based generalist radiology practices., Key Points: • There was no significant difference in MR image interpretation accuracy between generalist and abdominal fellowship-trained radiologists when evaluating sensitivity (p = 1.0) and specificity (p = 0.53). • There was no significant difference in accuracy comparing MR to CT imaging for diagnosing appendicitis for either sensitivity (p = 0.21) or specificity (p = 0.17). • With experience, generalist radiologists enhanced their MR interpretation accuracy as demonstrated by improved interpretation sensitivity (OR 2.89 CI 1.44-5.77, p = 0.003) and decreased mean interpretation time (5 to 3.89 min)., (© 2021. European Society of Radiology.)

Colorectal cancer (CRC) is a leading cause of cancer death in the United States. Standard treatment for advanced-stage CRC for decades has included 5-fluorouracil-based chemotherapy. More recently, targeted therapies for metastatic CRC are being used based on the individual cancer's molecular profile. In the past few years, several different molecular subtype schemes for human CRC have been developed. The molecular subtypes can be distinguished by gene expression signatures and have the potential to be used to guide treatment decisions. However, many subtyping classification methods were developed using mRNA expression levels of hundreds to thousands of genes, making them impractical for clinical use. In this study, we assessed whether an immunohistochemical approach could be used for molecular subtyping of CRCs. We validated two previously published, independent sets of immunohistochemistry classifiers and modified the published methods to improve the accuracy of the scoring methods. In addition, we evaluated whether protein and genetic signatures identified originally in the mouse were linked to clinical outcomes of patients with CRC. We found that low DDAH1 or low GAL3ST2 protein levels in human CRCs correlate with poor patient outcomes. The results of this study have the potential to impact methods for determining the prognosis and therapy selection for patients with CRC., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Among 109 iohexol-based water-soluble contrast (WSC) challenges performed for suspected small-bowel obstruction, 105 were technically adequate. Among technically adequate studies, colonic contrast (i.e., successful challenge) was seen on 66 abdominal radiographs obtained 8 hours after WSC challenge and 86 abdominal radiographs obtained 24 hours after WSC challenge. Fourteen patients underwent operative management, and 91 underwent nonoperative management (NOM). Successful challenge had a sensitivity of 91.2%, specificity of 78.5%, PPV of 96.5%, NPV of 57.8%, and odds ratio of 38.0 (95% CI, 8.7-165.2) for NOM. Three of 86 patients with successful challenge underwent operative management.

Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation., (© RSNA, 2021.)

Background: Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical., Purpose: To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT., Materials and Methods: The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations., Results: On CTC scans, automated Agatston scoring correlated highly with manual assessment (R 2  = 0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R 2  = 0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments., Conclusion: Fully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies., (Published by Elsevier Inc.)

Background: Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of both pre- and postcontrast CT number., Purpose: We propose an automatable solution to quantifying contrast enhancement without requiring a dedicated noncontrast series., Methods: The difference in CT number between a target region in an enhanced and unenhanced image defines the metric "quantification of iodine contrast enhancement" (Q-ICE). Quantification of iodine contrast enhancement uses the noncontrast bolus tracking baseline image from routine abdominal examinations, which mitigates the need for a dedicated noncontrast series. We applied this method retrospectively to 312 patient livers from 2 sites between 2017 and 2020. Each site used a weight-based contrast injection protocol for weights 60 to 113 kg and a constant volume less than 60 kg and greater than 113 kg. Hypothesis testing was performed to compare Q-ICE between sites and detect Q-ICE dependence on weight and kilovoltage (kV)., Results: Mean Q-ICE differed between sites (P = 0.004) by 4.96 Hounsfield unit with 95% confidence interval (1.63-8.28), albeit this difference was roughly 2 times smaller than the SD in Q-ICE across patients at a single site. For patients between 60 and 113 kg, we did not observe evidence of Q-ICE varying with patient weight (P = 0.920 and 0.064 for 120 and 140 kV, respectively). The Q-ICE did vary with patient weight for patients less than 60 kg (P = 0.003) and greater than 113 kg (P = 0.04). We observed a roughly 10 Hounsfield unit reduction in Q-ICE liver for patients scanned with 140 versus 120 kV. We observed several underenhancing examinations with an arterial phase appearance motivating our CT protocol optimization team to consider increasing the delay for slowly enhancing patients., Conclusions: A quality metric for quantifying CT contrast enhancement was developed and suggested tangible opportunities for quality improvement and potential financial savings., Competing Interests: T.P.S. has a United States Patent Application on methods described in this work. All the other authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Tuberous sclerosis complex (TSC) is a relatively rare autosomal dominant neurocutaneous disorder secondary to mutations in the TSC1 or TSC2 tumor suppressor genes. Although manifestation of the classic triad of seizures, intellectual disability, and facial angiofibromas may facilitate timely diagnosis of TSC, the multisystem features that may indicate TSC in the absence of these manifestations remain highly variable. In addition, patients with TSC are at risk of developing multiple benign and malignant tumors in various organ systems, resulting in increased morbidity and mortality. Thus, imaging plays a critical role in diagnosis, surveillance, and management of patients with TSC. It is crucial that radiologists be familiar with TSC and the various associated imaging features to avoid a delayed or incorrect diagnosis. Key manifestations include cortical dysplasias, subependymal nodules, subependymal giant cell astrocytomas, cardiac rhabdomyomas, lymphangioleiomyomatosis, and angiomyolipomas. Renal angiomyolipomas in particular can manifest with imaging features that mimic renal malignancy and pose a diagnostic dilemma. Other manifestations include dermatologic and ophthalmic manifestations, renal cysts, renal cell carcinomas, multifocal micronodular pneumocyte hyperplasia, splenic hamartomas, and other rare tumors such as perivascular epithelioid tumors. In addition to using imaging and clinical features to confirm the diagnosis, genetic testing can be performed. In this article, the molecular pathogenesis, clinical manifestations, and imaging features of TSC are reviewed. Current recommendations for management and surveillance of TSC are discussed as well. © RSNA, 2021.

BACKGROUND. The greater omentum can serve as a useful target for percutaneous biopsy; in clinical practice, CT is commonly used for biopsy guidance. OBJECTIVE. The purpose of this study was to evaluate the diagnostic yield of percutaneous ultrasound (US)-guided omental biopsy and to explore the association of the diagnostic yield with prebiopsy diagnostic CT findings. METHODS. This retrospective study included 163 patients (120 women and 43 men; mean age, 65 ± 12 [SD] years; mean body mass index [BMI], 28.9 ± 7.9) who underwent US-guided omental biopsy between 2002 and 2020 at a single institution at which US served as the first-line modality for omental biopsy guidance. Biopsies were performed by abdominal radiologists without dedicated interventional radiology fellowship training. Postbiopsy clinical follow-up and imaging follow-up were reviewed to establish the ultimate diagnosis for each patient. Omental biopsies were characterized as diagnostic or nondiagnostic relative to the ultimate diagnosis. Associations were explored between diagnostic yield and findings on prebiopsy CT and biopsy US. RESULTS. US-guided omental biopsy was performed using an 18-gauge core needle biopsy technique in 156 patients and fine-needle aspiration in seven patients. The mean number of biopsy passes was 2.5 ± 1.0, and mean omental thickness near the biopsy site on CT was 2.6 ± 1.2 cm. On prebiopsy diagnostic CT, omental disease appeared infiltrative in 127 (78%) patients versus mass-forming in 36 (22%) and appeared hypoechoic in 105 (64%) patients versus iso- to hyperechoic in 58 (36%). The ultimate diagnosis was malignant tumor in 154 (95%) patients (most commonly, gynecologic tumors in 82 patients [high-grade serous adenocarcinoma in 56] and gastrointestinal tumors in 45 patients) and a benign finding in nine (6%) patients. The omental biopsy was diagnostic relative to the ultimate diagnosis in 155 (95%) patients. A diagnostic versus nondiagnostic biopsy was not associated ( p > .05) with age, BMI, number of biopsy passes, or omental target thickness or attenuation. A total of 94% (120/127) of US-guided omental biopsies of infiltrative cases and 97% (35/36) of biopsies of mass-forming cases were diagnostic ( p = .50). A total of 96% (102/106) of US-guided omental biopsies of hypoechoic cases and 93% (53/57) of biopsies of iso- to hyperechoic cases were diagnostic ( p = .36). No complications occurred. CONCLUSION. US-guided biopsy of omental disease suspected on CT is safe and effective for tissue diagnosis. Although omental disease commonly appears on US as diffuse infiltrative thickening without a discrete target, sampling based on prebiopsy CT landmarks is diagnostic in most cases. CLINICAL IMPACT. US should be considered the first-line modality for omental biopsy guidance when feasible.

BACKGROUND. Noninvasive tests for colorectal cancer (CRC) screening and prevention limit the need for invasive colonoscopy to follow up positive test results. However, the relative performance characteristics of available noninvasive tests have not yet been adequately compared. OBJECTIVE. We performed a systematic review and meta-analysis to compare the diagnostic performance of the available noninvasive CRC screening tests, including multitarget stool DNA (mt-sDNA) testing, fecal immunochemical testing (FIT), and CT colonography (CTC), with an emphasis on comparison of PPV and detection rate (DR) for advanced neoplasia (AN; encompassing cases of advanced adenomas and CRC). EVIDENCE ACQUISITION. After systematic searches of MEDLINE and Google Scholar databases, 10 mt-sDNA, 27 CTC, and 88 FIT published screening studies involving 25,132, 33,493, and 2,355,958 asymptomatic adults, respectively, were included. Meta-analysis with hierarchic Bayesian modeling was conducted in accordance with Cochrane Collaboration and PRISMA guidelines to determine test positivity rates (TPRs) leading to optical colonoscopy, as well as PPVs and DRs for both AN and CRC. Different positivity thresholds were considered for FIT and CTC. EVIDENCE SYNTHESIS. Point estimates (with 95% credible intervals) from pooled Bayesian meta-analysis combining all thresholds for FIT and stratifying CTC results by a polyp size threshold of 6 mm or larger (CTC6) and 10 mm or larger (CTC10) were calculated. TPR was 13.5% (10.9-16.6%) for mt-sDNA testing, 6.4% (5.8-7.2%) for FIT, 13.4% (11.4-15.6%) for CTC6, and 6.6% (5.2-7.7%) for CTC10. AN PPV was 26.9% (95% credible interval, 21.8-33.2%) for mt-sDNA testing, 31.8% (29.3-34.5%) for FIT, 34.4% (27.2-41.0%) for CTC6, and 61.0% (54.0-70.0%) for CTC10. CRC PPV was 2.4% (1.5-3.9%) for mt-sDNA testing, 4.9% (4.3-5.3%) for FIT, 3.5% (2.5-4.8%) for CTC6, and 6.0% (4.3-8.0%) for CTC10. The DR for AN was 3.4% (95% credible interval, 2.5-4.8%) for mt-SDNA, 2.0% (1.8-2.3%) for FIT, 4.8% (4.0-6.5%) for CTC6, and 4.0% (3.0-4.6%) for CTC10. When FIT is restricted to a lower threshold (< 10 μg Hb/g feces), its performance profile is similar to that of mt-sDNA testing, although available data are limited. AN PPV odds ratios (relative to CTC10 as the reference) were 0.24 (95% credible interval, 0.17-0.33) for mt-sDNA testing, 0.30 (0.24-0.45) for FIT, and 0.33 (0.25-0.47) for CTC6. CONCLUSION. Among noninvasive CRC screening tests, CTC with a polyp size threshold of 10 mm or larger most effectively targets AN, preserving detection while also decreasing unnecessary colonoscopies compared with mt-sDNA testing and FIT. CLINICAL IMPACT. CTC performed with a polyp size threshold for colonoscopy referral set at 10 mm or larger represents the most effective and efficient noninvasive screening test for CRC prevention and detection.

Computed tomography (CT) scout images, also known as CT localizer radiographs, topograms, or scanograms, are an important, albeit often overlooked part of the CT examination. Scout images may contain important findings outside of the scanned field of view on CT examinations of the abdomen and pelvis, such as unsuspected lung cancer at the lung bases. Alternatively, scout images can provide complementary information to findings within the scanned field of view, such as characterization of retained surgical foreign bodies. Assessment of scout images adds value and provides a complementary "opportunistic" review for interpretation of abdominopelvic CT examinations. Scout image review is a useful modern application of conventional abdominal radiograph interpretation that can help establish a diagnosis or narrow a differential diagnosis. This review discusses the primary purpose and intent of the CT scout images, addresses standard of care and bias related to scout image review, and presents a general systematic approach to assessing scout images with multiple illustrative examples, including potential pitfalls in interpreting scout images., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Cystic hepatobiliary neoplasms with mucin-producing epithelium-mucinous cystic neoplasm of the liver (MCN) and intraductal papillary neoplasm of the bile duct (IPNB)-are rare and distinct entities that have unique clinical, pathologic, and imaging features. They are differentiated pathologically by the presence of subepithelial ovarian-like hypercellular stroma (OLS), which is the defining histopathologic feature of MCN. MCN is commonly a benign, large, solitary, symptomatic, multiloculated cystic mass without biliary communication that occurs in middle-aged women. On the other hand, IPNBs are a heterogeneous spectrum of tumors, which are commonly associated with invasive carcinoma, occur in older patients, and can be differentiated from MCN by communication with the biliary tree, intraductal masses, associated biliary ductal dilatation, and absent OLS. Understanding of these rare neoplasms has grown and evolved over time and continues to today, but uncertainty and controversy persist, related to the rarity of these tumors, relatively recent designation as separate entities, inherent clinicopathologic heterogeneity, overlapping imaging features, and the fact that many prior studies likely included MCN and cystic IPNB together as a single entity. Confusion regarding these neoplasms is evident by historical inconsistencies and nonstandardized nomenclature through the years. Awareness of these entities is important for the interpreting radiologist to suggest a particular diagnosis or generate a meaningful differential diagnosis in the appropriate setting, and is of particular significance as MCN and cystic IPNB have overlapping imaging features with other more common hepatobiliary cystic masses but have different management and prognosis. Online supplemental material is available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.

Amyloidosis is a group of conditions defined by extracellular deposition of insoluble proteins that can lead to multiorgan dysfunction and failure. The systemic form of the disease is often associated with a plasma cell dyscrasia but may also occur in the setting of chronic inflammation, long-term dialysis, malignancy, or multiple hereditary conditions. Localized forms of the disease most often involve the skin, tracheobronchial tree, and urinary tract and typically require tissue sampling for diagnosis, as they may mimic many conditions including malignancy at imaging alone. Advancements in MRI and nuclear medicine have provided greater specificity for the diagnosis of amyloidosis involving the central nervous system and heart, potentially obviating the need for biopsy of the affected organ in certain circumstances. Specifically, a combination of characteristic findings at noninvasive cardiac MRI and skeletal scintigraphy in patients without an underlying plasma cell dyscrasia is diagnostic for cardiac transthyretin amyloidosis. Histologically, the presence of amyloid is denoted by staining with Congo red and a characteristic apple green birefringence under polarized light microscopy. The imaging features of amyloid vary across each organ system but share some common patterns, such as soft-tissue infiltration and calcification, that may suggest the diagnosis in the appropriate clinical context. The availability of novel therapeutics that target amyloid protein fibrils such as transthyretin highlights the importance of early diagnosis. Online supplemental material is available for this article. © RSNA, 2021.

Purpose: To evaluate the outcomes of computed tomography (CT) fluoroscopy-guided core lung biopsies with emphasis on diagnostic yield, complications, and efficacy of parenchymal and pleural blood patching to avoid chest tube placement., Methods: This is a single-center retrospective analysis of CT fluoroscopy-guided percutaneous core lung biopsies between 2006 and 2020. Parenchymal blood patching during introducer needle withdrawal was performed in 74% of cases as a preventive measure, and pleural blood patching was the primary salvage maneuver for symptomatic or growing pneumothorax in 60 of 83 (72.2%) applicable cases., Results: A total of 1,029 patients underwent 1,112 biopsies (532 men; mean age, 66 years; 38.6%, history of emphysema; lesion size, 16.7 mm). The diagnostic yield was 93.6% (1,032/1,103). Fewer complications requiring intervention were observed in patients who underwent parenchymal blood patching (5.7% vs 14.2%, P < .001). Further intervention was required in 83 of 182 pneumothorax cases, which included the following: (a) pleural blood patch (5.4%, 60/1,112), (b) chest tube placement without a pleural blood patch attempt (1.5%, 17/1,112), and (c) simple aspiration (0.5%, 6/1,112). Pleural blood patch as monotherapy was successful in 83.3% (50/60) of cases without need for further intervention. The overall chest tube rate was 2.6% (29/1,112). Emphysema was the only significant risk factor for complications requiring intervention (P ≤ .001)., Conclusions: Parenchymal blood patching during introducer needle withdrawal decreased complications requiring intervention. Salvage pleural blood patching reduced the frequency of chest tube placement for pneumothorax., (Copyright © 2021 SIR. Published by Elsevier Inc. All rights reserved.)

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% ( n = 603), 12.5% ( n = 151) and 4.8% ( n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Purpose: Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver., Methods: An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis., Results: MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R 2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated., Conclusions: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms., (© 2021 American Association of Physicists in Medicine.)

Objectives: We sought to determine the diagnostic accuracy of magnetic resonance (MR) imaging compared with computed tomography (CT) and ultrasound (US) when evaluating for five common pelvic pathologies among women presenting to the emergency department (ED) with right lower quadrant abdominal pain., Methods: This prospective, single-center study was conducted at an academic ED as a sub-analysis of a direct comparison of the diagnostic accuracy of CT and MR in the evaluation of appendicitis. Patients were eligible for participation in the parent study if they were at least 12 years old and had a CT performed for evaluation of possible appendicitis. In the current study, only female patients who also underwent pelvic US were included. Three radiologists independently interpreted each MR examination specifically for the presence of pelvic pathology, knowing that patients had initially undergone imaging evaluation for possible appendicitis. The determination of an independent expert panel of two radiologists and one emergency physician based on surgical pathology, comprehensive chart review, clinical information, and follow-up phone calls served as the reference standard. Test characteristics of MR, CT, and US were calculated based on this; the main outcome measure was the summary sensitivity and specificity of MR versus CT and US., Results: Forty-one participants were included with a mean age of 27.6 ± 10.8 years. The MR consensus interpretation had an overall sensitivity and specificity of 57.1% (CI 38.8-75.5%) and 97.2% (CI 94.7-99.6%) respectively, for detecting any of the five pelvic pathologies. By comparison, CT exhibited sensitivity and specificity of 66.7% (CI 50.0-83.5%) and 98.3% (CI 96.4-100.0%) while it was 64.3% (CI 46.5-82.0%) and 97.7% (CI 95.6-99.9%) for US, respectively. No significant differences were identified when comparing these modalities. Overall, Fleiss' kappa interrater reliability value for MR interpretation was 0.75, corresponding to substantial agreement between the three readers., Conclusions: In women who might otherwise undergo multiple imaging tests to evaluate gastrointestinal versus pelvic pathologies, our data suggest that MR may be an acceptable first-line imaging test., (© 2021. American Society of Emergency Radiology.)

Objectives: Intestinal malrotation is largely a pediatric diagnosis, but initial detection can be made in adulthood. CT colonography (CTC) provides an ideal means for estimating prevalence. Our purpose was to evaluate the prevalence and imaging findings of intestinal malrotation in asymptomatic adults at CTC screening, as well as incomplete optical colonoscopy (OC) referral., Methods: The CTC database of a single academic institution was searched for cases of intestinal malrotation (developmental nonrotation). Prevalence was estimated from 11,176 adults undergoing CTC. Demographic, clinical, imaging (CTC and other abdominal exams), and surgical data were reviewed., Results: 27 cases of malrotation were confirmed (mean age 62 ± 9 years; 15 M/12F), including 17 from the CTC screening cohort (0.17% prevalence) and 10 from incomplete OC (0.75% prevalence; p < 0.001). Most cases (59%; 16/27) were initially diagnosed at CTC. In 67% (12/18); the presence of malrotation was missed on at least one relevant abdominal imaging examination. At least 22% (6/27) had a history of unexplained, chronic intermittent abdominal pain. At CTC, the SMA-SMV relationship was normal in only 11% (3/27). The ileocecal valve was located in the RLQ in only 22% (6/27). Two patients (7%) had associated findings of heterotaxy (polysplenia)., Conclusions: The prevalence of intestinal malrotation was four times greater for patients referred from incomplete OC compared with primary screening CTC, likely related to anatomic challenges at endoscopy. Malrotation was frequently missed at other abdominal imaging examinations. CTC can uncover unexpected cases of malrotation in adults, which may be relevant in terms of potential for future complications.

Background: Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes., Methods: Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events., Results: Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction., Conclusion: Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.