Correlation of HbA1c levels with CT-based body composition biomarkers in diabetes mellitus and metabolic syndrome.

Diabetes mellitus and metabolic syndrome are closely linked with visceral body composition, but clinical assessment is limited to external measurements and laboratory values including hemoglobin A1c (HbA1c). Modern deep learning and AI algorithms allow automated extraction of biomarkers for organ size, density, and body composition from routine computed tomography (CT) exams. Comparing visceral CT biomarkers across groups with differing glycemic control revealed significant, progressive CT biomarker changes with increasing HbA1c. For example, in the unenhanced female cohort, mean changes between normal and poorly-controlled diabetes showed: 53% increase in visceral adipose tissue area, 22% increase in kidney volume, 24% increase in liver volume, 6% decrease in liver density (hepatic steatosis), 16% increase in skeletal muscle area, and 21% decrease in skeletal muscle density (myosteatosis) (all p < 0.001). The multisystem changes of metabolic syndrome can be objectively and retrospectively measured using automated CT biomarkers, with implications for diabetes, metabolic syndrome, and GLP-1 agonists.

Intrapatient Changes in CT-Based Body Composition After Initiation of Semaglutide (Glucagon-Like Peptide-1 Agonist) Therapy.

Background: The long-acting glucagon-like peptide-1 receptor agonist semaglutide is used to treat type 2 diabetes or obesity in adults. Clinical trials have observed associations of semaglutide with weight loss, improved diabetic control, and cardiovascular risk reduction. Objective: To evaluate intrapatient changes in body composition after initiation of semaglutide therapy by applying an automated suite of CT-based artificial intelligence (AI) body composition tools. Methods: This retrospective study included adult patients with semaglutide treatment who underwent abdominopelvic CT both within 5 years before and within 5 years after semaglutide initiation, between January 2016 and November 2023. An automated suite of previously validated CT-based AI body composition tools was applied to pre-semaglutide and post-semaglutide scans to quantify visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) area, skeletal muscle area and attenuation, intermuscular adipose tissue (IMAT) area, liver volume and attenuation, and trabecular bone mineral density (BMD). Patients with ≥5-kg weight loss and ≥5-kg weight gain between scans were compared. Results: The study included 241 patients (mean age, 60.4±12.4 years; 151 women, 90 men). In the weight-loss group (n=67), the post-semaglutide scan, versus pre-semaglutide scan, showed decrease in VAT area (341.1 vs 309.4 cm2, p<.001), SAT area (371.4 vs 410.7 cm2, p<.001), muscle area (179.2 vs 193.0, p<.001), and liver volume (2379.0 vs 2578 HU, p=.009), and increase in liver attenuation (74.5 vs 67.6 HU, p=.03). In the weight-gain group (n=48), the post-semaglutide scan, versus pre-semaglutide scan, showed increase in VAT area (334.0 vs 312.8, p=.002), SAT area (485.8 vs 488.8 cm2, p=.01), and IMAT area (48.4 vs 37.6, p=.009), and decrease in muscle attenuation (5.9 vs 13.1, p<.001). Other comparisons were not significant (p>.05). Conclusion: Patients using semaglutide who lost versus gained weight demonstrated distinct patterns of changes in CT-based body composition measures. Those with weight loss exhibited overall favorable shifts in measures related to cardiometabolic risk. Muscle attenuation decrease in those with weight gain is consistent with decreased muscle quality. Clinical Impact: Automated CT-based AI tools provide biomarkers of body composition changes in patients using semaglutide beyond that which is evident by standard clinical measures.

Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue Elasticity in CT Images.

The elasticity of soft tissues has been widely considered a characteristic property for differentiation of healthy and lesions and, therefore, motivated the development of several elasticity imaging modalities, for example, ultrasound elastography, magnetic resonance elastography, and optical coherence elastography to directly measure the tissue elasticity. This paper proposes an alternative approach of modeling the elasticity for prior knowledge-based extraction of tissue elastic characteristic features for machine learning (ML) lesion classification using computed tomography (CT) imaging modality. The model describes a dynamic non-rigid (or elastic) soft tissue deformation in differential manifold to mimic the tissues' elasticity under wave fluctuation in vivo. Based on the model, a local deformation invariant is formulated using the 1st and 2nd order derivatives of the lesion volumetric CT image and used to generate elastic feature map of the lesion volume. From the feature map, tissue elastic features are extracted and fed to ML to perform lesion classification. Two pathologically proven image datasets of colon polyps and lung nodules were used to test the modeling strategy. The outcomes reached the score of area under the curve of receiver operating characteristics of 94.2% for the polyps and 87.4% for the nodules, resulting in an average gain of 5 to 20% over several existing state-of-the-art image feature-based lesion classification methods. The gain demonstrates the importance of extracting tissue characteristic features for lesion classification, instead of extracting image features, which can include various image artifacts and may vary for different protocols in image acquisition and different imaging modalities.

AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults.

OBJECTIVES: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population. METHODS: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile. RESULTS: A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT. CONCLUSION: Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures. CLINICAL RELEVANCE STATEMENT: Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients. KEY POINTS: Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.

Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis.

OBJECTIVE: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. MATERIALS AND METHODS: A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. RESULTS: The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). CONCLUSION: Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. CLINICAL RELEVANCE STATEMENT: Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. KEY POINTS: The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening.

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.

A Comparison of CT-Based Pancreatic Segmentation Deep Learning Models.

RATIONALE AND OBJECTIVES: Pancreas segmentation accuracy at CT is critical for the identification of pancreatic pathologies and is essential for the development of imaging biomarkers. Our objective was to benchmark the performance of five high-performing pancreas segmentation models across multiple metrics stratified by scan and patient/pancreatic characteristics that may affect segmentation performance. MATERIALS AND METHODS: In this retrospective study, PubMed and ArXiv searches were conducted to identify pancreas segmentation models which were then evaluated on a set of annotated imaging datasets. Results (Dice score, Hausdorff distance [HD], average surface distance [ASD]) were stratified by contrast status and quartiles of peri-pancreatic attenuation (5 mm region around pancreas). Multivariate regression was performed to identify imaging characteristics and biomarkers (n = 9) that were significantly associated with Dice score. RESULTS: Five pancreas segmentation models were identified: Abdomen Atlas [AAUNet, AASwin, trained on 8448 scans], TotalSegmentator [TS, 1204 scans], nnUNetv1 [MSD-nnUNet, 282 scans], and a U-Net based model for predicting diabetes [DM-UNet, 427 scans]. These were evaluated on 352 CT scans (30 females, 25 males, 297 sex unknown; age 58 ± 7 years [ ± 1 SD], 327 age unknown) from 2000-2023. Overall, TS, AAUNet, and AASwin were the best performers, Dice= 80 ± 11%, 79 ± 16%, and 77 ± 18%, respectively (pairwise Sidak test not-significantly different). AASwin and MSD-nnUNet performed worse (for all metrics) on non-contrast scans (vs contrast, P < .001). The worst performer was DM-UNet (Dice=67 ± 16%). All algorithms except TS showed lower Dice scores with increasing peri-pancreatic attenuation (P < .01). Multivariate regression showed non-contrast scans, (P < .001; MSD-nnUNet), smaller pancreatic length (P = .005, MSD-nnUNet), and height (P = .003, DM-UNet) were associated with lower Dice scores. CONCLUSION: The convolutional neural network-based models trained on a diverse set of scans performed best (TS, AAUnet, and AASwin). TS performed equivalently to AAUnet and AASwin with only 13% of the training set size (8488 vs 1204 scans). Though trained on the same dataset, a transformer network (AASwin) had poorer performance on non-contrast scans whereas its convolutional network counterpart (AAUNet) did not. This study highlights how aggregate assessment metrics of pancreatic segmentation algorithms seen in other literature are not enough to capture differential performance across common patient and scanning characteristics in clinical populations.

Abdominal CT findings characteristic of Castleman disease: multi-centre review of 76 adult cases with abdominopelvic nodal involvement.

OBJECTIVE: Characterize the CT findings of abdominopelvic Castleman disease, including a new observation involving the perinodal fat. METHODS: Multi-centre search at 5 institutions yielded 76 adults (mean age, 42.1 ± 14.3 years; 38 women/38 men) meeting inclusion criteria of histopathologically proven Castleman disease with nodal involvement at abdominopelvic CT. Retrospective review of the dominant nodal mass was assessed for size, attenuation, and presence of calcification, and for prominence and soft-tissue infiltration of the perinodal fat. Hypervascular nodal enhancement was based on both subjective and objective comparison with aortic blood pool attenuation. RESULTS: Abdominal involvement was unicentric in 48.7% (37/76) and multicentric in 51.3% (39/76), including 31 cases with extra-abdominal involvement. Histopathologic subtypes included hyaline vascular variant (HVV), plasma cell variant (PCV), mixed HVV/PCV, and HHV-8 variant in 39, 25, 3 and 9 cases, respectively. The dominant nodal mass measured 4.4 ± 1.9 cm and 3.2 ± 1.7 cm in mean long- and short-axis, respectively, and appeared hypervascular in 58.6% (41/70 with IV contrast). Internal calcification was seen in 22.4% (17/76). Infiltration of the perinodal fat, with or without hypertrophy, was present in 56.6% (43/76), more frequent with hypervascular vs non-hypervascular nodal masses (80.5% vs 20.7%; P < .001). Among HVV cases, 76.9% were unicentric, 71.1% appeared hypervascular, and 69.2% demonstrated perinodal fat infiltration. CONCLUSION: Hypervascular nodal masses demonstrating prominence and infiltration of perinodal fat at CT can suggest the specific diagnosis of Castleman disease, especially the HVV. ADVANCES IN KNOWLEDGE: Abdominopelvic nodal masses that demonstrate hypervascular enhancement and prominent infiltration of the perinodal fat at CT can suggest the diagnosis of Castleman disease, but nonetheless requires tissue sampling.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age [±SD], 60 years ± 11; 143 female), was tested on two internal datasets (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the F1/Dice coefficient, SDs, and 95% CIs, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved F1/Dice scores of 85.5% ± 6.1 (95% CI: 83.1, 87.8) and 82.6% ± 15.3 (95% CI: 76.4, 88.7), with median volume estimation errors of 19.6% (IQR, 13.2%-29.0%) and 5.3% (IQR: 2.4%-9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a F1/Dice score of 83.0% ± 10.7 (95% CI: 79.8, 86.3) and median volume estimation error of 9.7% (IQR, 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in patients with cirrhosis and those with ovarian cancer, in concordance with expert radiologist assessments. Keywords: Abdomen/GI, Cirrhosis, Deep Learning, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also commentary by Aisen and Rodrigues in this issue.

Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.

OBJECTIVE: Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging. METHODS: Final study cohort consisted of 1,612 older adults (mean age, 68.0 years; 594 women) all imaged utilizing a uniform CT urothelial protocol consisting of pre-contrast, portal venous, and delayed excretory phases. Fully-automated CT-based algorithms for quantifying numerous biomarkers, including muscle and fat area and density, bone mineral density, and solid organ volume were applied to pre-contrast and portal venous phases. The effect of IV contrast upon these body composition measures was analyzed. Regression analyses, including square of the Pearson correlation coefficient (r2), were performed for each comparison. RESULTS: We found that simple, linear relationships can be derived to determine non-contrast equivalent values from the post-contrast CT biomeasures. Excellent positive linear correlation (r2 = 0.91-0.99) between pre- and post-contrast values was observed for all automated soft tissue measures, whereas moderate positive linear correlation was observed for bone attenuation (r2 = 0.58-0.76). In general, the area- and volume-based measurement require less adjustment than attenuation-based measures, as expected. CONCLUSION: Fully-automated quantitative CT-biomarker measures at portal venous phase abdominal CT can be adjusted to a non-contrast equivalent using simple, linear relationships.

Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures.

OBJECTIVE: Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. METHODS: In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. RESULTS: Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. CONCLUSION: Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE: CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (n = 589) and internal test cohort (n = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, P < .001; C index = 0.689, P < .001) and performing comparably with senior radiologists (AUC = 0.79, P = .21; C index = 0.788, P = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, P < .001; C index = 0.798, P < .001) and senior radiologists (AUC = 0.88, P < .001; C index = 0.869, P < .001). Conclusion This study demonstrates the potential of WISDOM as a useful LN diagnosis method using routine rectal MRI data. The improved radiologist performance observed with model assistance highlights the potential clinical utility of WISDOM in practice. Keywords: MR Imaging, Abdomen/GI, Rectum, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. Published under a CC BY 4.0 license.

Metabolic syndrome: imaging features and clinical outcomes.

Metabolic syndrome, which affects around a quarter of adults worldwide, is a group of metabolic abnormalities characterized mainly by insulin resistance and central adiposity. It is strongly correlated with cardiovascular and all-cause mortality. Early identification of the changes induced by metabolic syndrome in target organs and timely intervention (eg, weight reduction) can decrease morbidity and mortality. Imaging can monitor the main components of metabolic syndrome and identify early the development and progression of its sequelae in various organs. In this review, we discuss the imaging features across different modalities that can be used to evaluate changes due to metabolic syndrome, including fatty deposition in different organs, arterial stiffening, liver fibrosis, and cardiac dysfunction. Radiologists can play a vital role in recognizing and following these target organ injuries, which in turn can motivate lifestyle modification and therapeutic intervention.

AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition measures associated with increased risk for all-cause mortality and adverse cardiovascular events. METHODS: Fully automated AI body composition tools quantifying abdominal aortic calcium, abdominal fat (visceral [VAT], visceral-to-subcutaneous ratio [VSR]), and muscle attenuation (muscle HU) were applied to non-contrast CT examinations in adults undergoing screening CT colonography (CTC). Patients were partitioned into 5 socioeconomic groups based on the national ADI rank at the census block group level. Pearson correlation analysis was performed to determine the association between national ADI and body composition measures. One-way analysis of variance was used to compare means across groups. Odds ratios (ORs) were generated using high-risk, high specificity (90% specificity) body composition thresholds with the most disadvantaged groups being compared to the least disadvantaged group (ADI < 20). RESULTS: 7785 asymptomatic adults (mean age, 57 years; 4361:3424 F:M) underwent screening CTC from April 2004-December 2016. ADI rank data were available in 7644 patients. Median ADI was 31 (IQR 22-43). Aortic calcium, VAT, and VSR had positive correlation with ADI and muscle attenuation had a negative correlation with ADI (all p < .001). Compared with the least disadvantaged group, mean differences for the most disadvantaged group (ADI > 80) were: Aortic calcium (Agatston) = 567, VAT = 27 cm2, VSR = 0.1, and muscle HU = -6 HU (all p < .05). Compared with the least disadvantaged group, the most disadvantaged group had significantly higher odds of having high-risk body composition measures: Aortic calcium OR = 3.8, VAT OR = 2.5, VSR OR = 2.0, and muscle HU OR = 3.1(all p < .001). CONCLUSION: Fully automated CT body composition tools show that socioeconomic disadvantage is associated with high-risk body composition measures and can be used to identify individuals at increased risk for all-cause mortality and adverse cardiovascular events.