Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms

Computed Tomography Colonography: 2025 Update

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Most cases arise from polyps, which can be detected and removed before becoming cancerous. Computed tomography colonography (CTC), also known as virtual colonoscopy, was first introduced in 1994 as a minimally invasive method for CRC screening and diagnosis. This 2025 update on CTC will focus on (1) techniques and dose reduction strategies, (2) image display methods, (3) reporting and classification systems, (4) tumor staging capabilities, (5) integration of advanced imaging techniques, and (6) cost-effectiveness and reimbursement.

Impact of Unfortified Human Milk, Fortified Human Milk, and Preterm Formula Intake on Body Composition at Term in Very Preterm Infants: Secondary Analysis of the PREMFOOD Trial

Background/Objectives: Very preterm body composition at term shows potential as a biomarker of later health outcomes, but effects from in-hospital formula versus human milk (HM) (maternal milk (MM) and/or pasteurised human donor milk (DM) supplement) intake studies are confounded by the effect from the fortifier. We investigated the impact of in-hospital unfortified HM (UHM), fortified HM (FHM), and preterm formula (PTF) intake on very preterm body composition at term. Methods: Preplanned analysis of the PREterM FOrmula or Donor milk (PREMFOOD) trial: Infants born at <32 weeks were randomised to either (i) UHM, (ii) FHM, or (iii) MM and/or PTF supplement. Main outcomes were assessed by anthropometry and magnetic resonance imaging of body composition at term. Secondary comparison between groups defined by proportion of milk intake from birth to 35 weeks postmenstrual age: The groups comprised exclusive UHM (ExUHM, proportion of UHM 99-100%, n = 23), predominantly UHM (PrUHM, UHM 50-98.9%, n = 15), predominantly FHM (PrFHM, FHM > 50%, n = 17), and predominantly PTF (PrPTF, PTF > 50%, n = 7). Results: At term, compared to the ExUHM group, the PrPTF group had 274.3 g (95% CI: 30.1 to 518.5) more Non-Adipose Tissue Mass (NATM) and a 1.11 times (95% CI: 0.38 to 1.84) greater increase in weight z score from birth, while both PrPTF and PrFHM had greater increases in length z scores from birth. Conclusions: High formula intake was associated with maximal gains in NATM at term, and these gains were not matched by the early fortification of HM. The alteration of body composition at term with prolonged or delayed HM fortification and its relation to later health outcomes are important research questions.

Pancreatic changes in patients with visceral fat obesity: an evaluation with contrast-enhanced dual-energy computed tomography with automated three-dimensional volumetry

PURPOSE

To investigate pancreatic changes associated with visceral fat obesity (VFO) and their clinical relevance using contrast-enhanced dual-energy CT (DE-CT) with automated 3D volumetry.

METHODS

This retrospective study included patients who underwent triple-phase contrast-enhanced dynamic abdominal DE-CT. The patients were divided into two groups based on the measured visceral fat area: the VFO group (≥ 100 cm2) and the non-VFO group (< 100 cm2). Pancreatic changes in 3D CT volumetric measurement parameters were evaluated.

RESULTS

In total, 119 patients were evaluated (mean age, 67.6 ± 12.9 years old; 80 men). The extracellular volume fraction calculated from iodine maps (ECV-ID) (r = -0.683, p < 0.001) was most strongly associated with the visceral fat area, followed by the fat volume fraction (FVF) of the pancreas (r = 0.582, p < 0.001) with a statistically moderate correlation. The pancreatic volume and FVF of the pancreas were significantly higher in the VFO group than in the non-VFO group (volume: 84.9 ± 22.9 vs. 76.5 ± 25.8, p = 0.025, FVF: 15.5 ± 7.7 vs. 8.7 ± 9.5, p < 0.001). Conversely, the pancreatic CT attenuation value on unenhanced CT (19.9 ± 12.0 vs. 29.6 ± 13.8, p < 0.001), pancreatic iodine concentration in the equilibrium phase (EP) (18.4 ± 5.7 vs. 19.8 ± 4.7, p = 0.003), contrast enhancement (CE) value of pancreas (32.2 ± 5.3 vs. 34.5 ± 8.5, p = 0.005), and ECV-ID (26.7 ± 5.4 vs. 34.1 ± 7.4, p < 0.001) in the VFO group were significantly lower than those in the non-VFO group.

CONCLUSION

An increase in the pancreatic volume and FVF of the pancreas, as well as a reduction in the ECV fraction and the CE value in EP of the pancreas measured by automated 3D DE-CT volumetry, were the characteristic pancreatic changes in patients with VFO.

Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis

PURPOSE

To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs.

METHODS

Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC).

RESULTS

The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement.

CONCLUSION

Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.

CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients

BACKGROUND. CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now allow deployment of tools that measure body composition in large patient populations. OBJECTIVE. The purpose of this study was to assess associations of age, sex, and common systemic diseases with CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample. METHODS. This retrospective study included 140,606 adult patients (67,613 men and 72,993 women; mean age, 53.1 ± 17.6 [SD] years) who underwent abdominal CT at a single academic institution between January 1, 2000, and February 28, 2021. CT examinations were not restricted on the basis of patient setting, clinical indication, or IV contrast media use. Thirteen fully automated AI body composition tools quantifying liver, spleen, and kidney volume and attenuation; vertebral trabecular attenuation; skeletal muscle area and attenuation; and abdominal fat area and attenuation were applied to each patient's first available abdominal CT examination. EHR review was performed to identify common systemic diseases, including cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis, on the basis of relevant ICD-10 codes; 64,789 patients (46.1%) had at least one systemic disease diagnosed. Multiple linear regression models were performed for the 118,141 patients (84.0%) with no systemic disease or a single systemic disease, to assess age, sex, and the presence of systemic disease as predictors of body composition measures; effect sizes were characterized using the unstandardized regression coefficient B. RESULTS. Multiple linear regression models using age, sex, and systemic disease as predictors were overall significant for all 13 body composition measures (all p < .001) with variable goodness of fit (R2 = 0.03-0.43 across models). In the models, age was predictive of all 13 body composition measures; sex, 12 measures; cancer, nine measures; CVD, 11 measures; DM, 13 measures; and cirrhosis, 12 measures (all p < .05). CONCLUSION. Age, sex, and the presence of common systemic diseases were predictors of AI-derived CT-based body composition measures. CLINICAL IMPACT. An understanding of the identified associations with common systemic diseases will be critical for establishing normative reference ranges as CT-based AI body composition tools are developed for clinical use.

Association of visceral fat obesity with structural change in abdominal organs: fully automated three-dimensional volumetric computed tomography measurement using deep learning

The purpose of this study was to explore the association between structural changes in abdominal organs and visceral fat obesity (VFO) using a fully automated three-dimensional (3D) volumetric computed tomography (CT) measurement method based on deep learning algorithm. A total of 610 patients (295 men and 315 women; mean age, 68.4 years old) were included. Fully automated 3D volumetric CT measurements of the abdominal organs were performed to determine the volume and average CT attenuation values of each organ. All patients were divided into 2 groups based on the measured visceral fat area: the VFO group (≥ 100 cm2) and non-VFO group (< 100 cm2), and the structural changes in abdominal organs were compared between these groups. The volumes of all organs were significantly higher in the VFO group than in the non-VFO group (all of p < 0.001). Conversely, the CT attenuation values of all organs in the VFO group were significantly lower than those in the non-VFO group (all of p < 0.001). Pancreatic CT values (r = - 0.701, p < 0.001) were most strongly associated with the visceral fat, followed by renal CT values (r = - 0.525, p < 0.001) and hepatic CT values (r = - 0.510, p < 0.001). Fully automated 3D volumetric CT measurement using a deep learning algorithm has the potential to detect the structural changes in the abdominal organs, especially the pancreas, such as an increase in the volumes and a decrease in CT attenuation values, probably due to increased ectopic fat accumulation in patients with VFO. This technique may provide valuable imaging support for the early detection and intervention of metabolic-related diseases.

Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.3 years). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment.

Opportunistic Assessment for Parathyroid Adenoma on CT: A Retrospective Cohort Study Evaluating Primary Hyperparathyroidism-Associated Morbidity Over 10 Years of Follow-Up

BACKGROUND. Primary hyperparathyroidism (PHPT) is underdiagnosed. Opportunistic imaging-based parathyroid gland assessment is a proposed strategy for identifying patients at increased risk of undiagnosed PHPT. However, whether this approach is likely to identify individuals with clinically significant disease is unknown. OBJECTIVE. This study's objective was to assess for associations of the presence of an enlarged parathyroid gland on contrast-enhanced CT with clinical outcomes causally related to PHPT. METHODS. This retrospective cohort study included patients 18 years old or older with at least one contrast-enhanced chest or neck CT examination performed from January 2012 to December 2012, at least one noncontrast CT examination covering the chest or neck region without a date restriction, and at least one clinical encounter in the health system from January 2022 to December 2022. A neuroradiologist reviewed the CT examinations to determine the presence versus absence of an enlarged parathyroid gland on the 2012 study. Patient demographics, serum calcium results, and diagnosis codes for clinical outcomes causally related to PHPT were extracted from the EHR. Calcium results and diagnosis codes were classified as preexisting if documented before and as incident if documented after the 2012 contrast-enhanced CT examination. RESULTS. The cohort included 1198 patients (593 men and 605 women; mean age, 51.6 years), of whom 43 (3.6%) were assessed as having an enlarged parathyroid gland on the 2012 contrast-enhanced CT examination. PHPT was diagnosed in 16.3% of patients with, versus 0.3% of patients without, an enlarged parathyroid gland (p < .001). After adjustment for age, sex, race, and ethnicity, the presence of an enlarged parathyroid gland on contrast-enhanced CT was associated with significantly increased odds of preexisting nephrolithiasis (OR = 2.71; p = .03), hypercalcemia (OR = 5.30; p < .001), and PHPT (OR = 12.59; p = .008) as well as increased odds of incident osteopenia or osteoporosis (OR = 2.78; p = .008), nephrolithiasis (OR = 4.95; p < .001), hypercalcemia (OR = 7.58; p < .001), and PHPT (OR = 148.01; p < .001). CONCLUSION. An enlarged parathyroid gland indicated increased risk of PHPT as well as increased risk of preexisting and incident clinical conditions causally related to PHPT. CLINICAL IMPACT. Opportunistic CT-based assessment is a promising strategy for identifying patients at increased risk of undiagnosed PHPT; such assessment could potentially prevent some PHPT-related complications through earlier diagnosis and treatment.

Noninvasive Quantitative CT for Diffuse Liver Diseases: Steatosis, Iron Overload, and Fibrosis

Chronic diffuse liver disease continues to increase in prevalence and represents a global health concern. Noninvasive detection and quantification of hepatic steatosis, iron overload, and fibrosis are critical, especially given the many relative disadvantages and potential risks of invasive liver biopsy. Although MRI techniques have emerged as the preferred reference standard for quantification of liver fat, iron, and fibrosis, CT can play an important role in opportunistic detection of unsuspected disease and is performed at much higher volumes. For hepatic steatosis, noncontrast CT provides a close approximation to MRI-based proton-density fat fraction (PDFF) quantification, with liver attenuation values less than or equal to 40 HU signifying at least moderate steatosis. Liver fat quantification with postcontrast CT is less precise but can generally provide categorical assessment (eg, mild vs moderate steatosis). Noncontrast CT can also trigger appropriate assessment for iron overload when increased parenchymal attenuation values are observed (eg, >75 HU). A variety of morphologic and functional CT features indicate the presence of underlying hepatic fibrosis and cirrhosis. Beyond subjective assessment, quantitative CT methods for staging fibrosis can provide comparable performance to that of elastography. Furthermore, quantitative CT assessment can be performed retrospectively, since prospective techniques are not required. Many of these CT quantitative measures are now fully automated via artificial intelligence (AI) deep learning algorithms. These retrospective and automated advantages have important implications for longitudinal clinical care and research. Ultimately, regardless of the indication for CT, opportunistic detection of steatosis, iron overload, and fibrosis can result in appropriate clinical awareness and management. ©RSNA, 2024 See the invited commentary by Yeh in this issue.

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.

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.

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults

BACKGROUND

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality.

METHODS

The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141).

RESULTS

The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.

CONCLUSIONS

A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions

Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.

Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders

BACKGROUND

Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders.

METHODS

A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease.

RESULTS

For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77).

CONCLUSIONS

This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.

Evaluation for Performance of Body Composition Index Based on Quantitative Computed Tomography in the Prediction of Metabolic Syndrome

Objective: We aimed to evaluate the performance of predicting metabolic syndrome (MS) using body composition indices obtained by quantitative computed tomography (QCT). Methods: In this cross-sectional study, data were collected from 4745 adults who underwent QCT examinations at a Chongqing teaching hospital between July 2020 and March 2022. Visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), total abdominal fat (TAT), abdominal muscle tissue (AMT), and liver fat content (LFC) were measured at the L2-L3 disc level using specialized software, and the skeletal muscle index (SMI) were calculated. The correlations between body composition indicators were analyzed using the Pearson correlation analysis. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to assess these indicators' predictive potential for MS. Results: VAT and TAT exhibited the best predictive ability for MS, with AUCs of 0.797 [95% confidence interval (CI): 0.779-0.815] and 0.794 (95% CI: 0.775-0.812) in males, and 0.811 (95% CI: 0.785-0.836) and 0.802 (95% CI: 0.774-0.830) in females. The AUCs for VAT and TAT were the same but significantly higher than body mass index and other body composition measures. SAT also demonstrated good predictive power in females [AUC = 0.725 (95%CI: 0.692-0.759)] but fair power in males [AUC = 0.6673 (95%CI: 0.650-0.696)]. LFC showed average predictive ability, AMT showed average predictive ability in males but poor ability in females, and SMI had no predictive ability. Correlation analysis revealed a strong correlation between VAT and TAT (males: r = 0.95, females: r = 0.89). SAT was strongly correlated with TAT only in females (r = 0.89). In the male group, the optimal thresholds for VAT and TAT were 207.6 and 318.7 cm2, respectively; in the female group, the optimal thresholds for VAT and TAT were 128.0 and 269.4 cm2, respectively. Conclusions: VAT and TAT are the best predictors of MS. SAT and LFC can also be acceptable to make predictions, whereas AMT can only make predictions of MS in males.

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.

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.

Computed tomography evaluation of skeletal muscle quality and quantity in people with morbid obesity with and without metabolic abnormality

We investigated the differences in quantity and quality of skeletal muscle between metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) individuals using abdominal CT. One hundred and seventy-two people with morbid obesity who underwent bariatric surgery and 64 healthy control individuals participated in this retrospective study. We divided the people with morbid obesity into an MHO and MUO group. In addition, nonobese metabolic healthy people were included analysis to provide reference levels. CT evaluation of muscle quantity (at the level of the third lumbar vertebra [L3]) was performed by calculating muscle anatomical cross-sectional area (CSA), which was normalized to patient height to produce skeletal muscle index (SMI). Muscle quality was assessed as skeletal muscle density (SMD), which was calculated from CT muscle attenuation. To characterize intramuscular composition, muscle attenuation was classified into three categories using Hounsfield unit (HU) thresholds: -190 HU to -30 HU for intermuscular adipose tissue (IMAT), -29 to +29 HU for low attenuation muscle (LAM), and +30 to +150 HU for normal attenuation muscle (NAM). People with morbid obesity comprised 24 (14%) MHO individuals and 148 (86%) MUO individuals. The mean age of the participants was 39.7 ± 12.5 years, and 154 (65%) participants were women. MUO individuals had a significantly greater total skeletal muscle CSA than MHO individuals in the model that adjusted for all variables. Total skeletal muscle SMI, SMD, NAM index, LAM index, and IMAT index did not differ between MHO and MUO individuals for all adjusted models. Total skeletal muscle at the L3 level was not different in muscle quantity, quality, or intramuscular composition between the MHO and MUO individuals, based on CT evaluation. MHO individuals who are considered "healthy" should be carefully monitored and can have a similar risk of metabolic complications as MUO individuals, at least based on an assessment of skeletal muscle.

Artificial intelligence tool detection of intravenous contrast enhancement using spleen attenuation

PURPOSE

To assess the ability of an automated AI tool to detect intravenous contrast material (IVCM) in abdominal CT examinations using spleen attenuation.

METHODS

A previously validated automated AI tool measuring the attenuation of the spleen was deployed on a sample of 32,994 adult (age ≥ 18) patients (mean age, 61.9 ± 14.7 years; 13,869 men, 19,125 women) undergoing 65,449 supine position CT examinations (41,020 with and 24,429 without IVCM by DICOM header) from January 1, 2000 to December 31, 2021. After exclusions, receiver operating characteristic (ROC) curve analysis was performed to determine the optimal threshold for binary classification of IVCM status (non-contrast vs IVCM enhanced), which was then applied to the sample. Discordant examinations (i.e., IVCM status determined by AI tool did not match DICOM header) were manually reviewed to establish ground truth. Repeat ROC curve and contingency table analysis were performed to assess AI tool performance.

RESULTS

ROC analysis of the initial study sample of 61,783 CT examinations yielded AUC of 0.970 with Youden index suggesting an optimal spleen attenuation threshold of 65 Hounsfield units (HU). Manual review of 2094 discordant CT examinations revealed discordance due to DICOM header error in 1278 (61.0%) and AI tool misclassification in 410 (19.6%), with 406 (9.4%) meeting exclusion criteria. Analysis of 61,377 CT examinations in the final study sample yielded AUC of 0.999 with accuracy of 99.3% at the 65 HU threshold. Error rate for DICOM header information was 2.1% (1278/61,377) versus 0.7% (410/61,377) for the AI tool.

CONCLUSION

The automated spleen attenuation AI tool was highly accurate for detection of IVCM at a threshold of 65 HU.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.

Technical Adequacy of Fully Automated Artificial Intelligence Body Composition Tools: Assessment in a Heterogeneous Sample of External CT Examinations

BACKGROUND. Clinically usable artificial intelligence (AI) tools analyzing imaging studies should be robust to expected variations in study parameters. OBJECTIVE. The purposes of this study were to assess the technical adequacy of a set of automated AI abdominal CT body composition tools in a heterogeneous sample of external CT examinations performed outside of the authors' hospital system and to explore possible causes of tool failure. METHODS. This retrospective study included 8949 patients (4256 men, 4693 women; mean age, 55.5 ± 15.9 years) who underwent 11,699 abdominal CT examinations performed at 777 unique external institutions with 83 unique scanner models from six manufacturers with images subsequently transferred to the local PACS for clinical purposes. Three independent automated AI tools were deployed to assess body composition (bone attenuation, amount and attenuation of muscle, amount of visceral and sub-cutaneous fat). One axial series per examination was evaluated. Technical adequacy was defined as tool output values within empirically derived reference ranges. Failures (i.e., tool output outside of reference range) were reviewed to identify possible causes. RESULTS. All three tools were technically adequate in 11,431 of 11,699 (97.7%) examinations. At least one tool failed in 268 (2.3%) of the examinations. Individual adequacy rates were 97.8% for the bone tool, 99.1% for the muscle tool, and 98.9% for the fat tool. A single type of image processing error (anisometry error, due to incorrect DICOM header voxel dimension information) accounted for 81 of 92 (88.0%) examinations in which all three tools failed, and all three tools failed whenever this error occurred. Anisometry error was the most common specific cause of failure of all tools (bone, 31.6%; muscle, 81.0%; fat, 62.8%). A total of 79 of 81 (97.5%) anisometry errors occurred on scanners from a single manufacturer; 80 of 81 (98.8%) occurred on the same scanner model. No cause of failure was identified for 59.4% of failures of the bone tool, 16.0% of failures of the muscle tool, or 34.9% of failures of the fat tool. CONCLUSION. The automated AI body composition tools had high technical adequacy rates in a heterogeneous sample of external CT examinations, supporting the generalizability of the tools and their potential for broad use. CLINICAL IMPACT. Certain causes of AI tool failure related to technical factors may be largely preventable through use of proper acquisition and reconstruction protocols.

AI-based opportunistic CT screening of incidental cardiovascular disease, osteoporosis, and sarcopenia: cost-effectiveness analysis

PURPOSE

To assess the cost-effectiveness and clinical efficacy of AI-assisted abdominal CT-based opportunistic screening for atherosclerotic cardiovascular (CV) disease, osteoporosis, and sarcopenia using artificial intelligence (AI) body composition algorithms.

METHODS

Markov models were constructed and 10-year simulations were performed on hypothetical age- and sex-specific cohorts of 10,000 U.S. adults (base case: 55 year olds) undergoing abdominal CT. Using expected disease prevalence, transition probabilities between health states, associated healthcare costs, and treatment effectiveness related to relevant conditions (CV disease/osteoporosis/sarcopenia) were modified by three mutually exclusive screening models: (1) usual care ("treat none"; no intervention regardless of opportunistic CT findings), (2) universal statin therapy ("treat all" for CV prevention; again, no consideration of CT findings), and (3) AI-assisted abdominal CT-based opportunistic screening for CV disease, osteoporosis, and sarcopenia using automated quantitative algorithms for abdominal aortic calcification, bone mineral density, and skeletal muscle, respectively. Model validity was assessed against published clinical cohorts.

RESULTS

For the base-case scenarios of 55-year-old men and women modeled over 10 years, AI-assisted CT-based opportunistic screening was a cost-saving and more effective clinical strategy, unlike the "treat none" and "treat all" strategies that ignored incidental CT body composition data. Over a wide range of input assumptions beyond the base case, the CT-based opportunistic strategy was dominant over the other two scenarios, as it was both more clinically efficacious and more cost-effective. Cost savings and clinical improvement for opportunistic CT remained for AI tool costs up to $227/patient in men ($65 in women) from the $10/patient base-case scenario.

CONCLUSION

AI-assisted CT-based opportunistic screening appears to be a highly cost-effective and clinically efficacious strategy across a broad array of input assumptions, and was cost saving in most scenarios.

Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study

PURPOSE

To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D).

METHODS

Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset.

RESULTS

Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4].

CONCLUSION

Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.

CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation

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.

Estimation of visceral fat area using criteria for metabolic syndrome: A cross-sectional study

BACKGROUND AND AIMS

The aim of this study was to calculate the visceral fat area (VFA) based on the criteria for metabolic syndrome (MetS).

METHODS

A multiple regression analysis was performed to determine the estimated VFA using data from Japanese participants (2315 men and 1684 women). Receiver operating characteristic curve (ROC) analyses were performed to determine the optimal estimated VFA cutoff for the diagnosis of central obesity. The cutoff was also applied to a second cohort to validate the model.

RESULTS

The estimated VFA was calculated using the MetS criteria, age, and body mass index (adjusted coefficient of determination = 0.682 for men and 0.726 for women). The area under the ROC curve for waist circumference, VFA, and estimated VFA were 0.669, 0.741, and 0.749, respectively, for men and 0.711, 0.787, and 0.803, respectively, for women. The optimal cutoffs for estimated VFA were 128.1 cm² for men and 82.2 cm² for women. Multivariate logistic regression for heart disease revealed that estimated VFA, rather than waist circumference, was associated with a high risk of heart disease.

CONCLUSION

The estimated VFA is a better index of central obesity than waist circumference and VFA for the diagnosis of MetS.

Application of machine learning in liver transplantation: a review

BACKGROUND

Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk.

AIM

In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation.

METHODS

Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials.

CONCLUSION

ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.

Value-added Opportunistic CT Screening: State of the Art

Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning

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/m², 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.

Healthy US population reference values for CT visceral fat measurements and the impact of IV contrast, HU range, and spinal levels

Measurements of visceral adipose tissue cross-sectional area and radiation attenuation from computed tomography (CT) scans provide useful information about risk and mortality. However, scan protocols vary, encompassing differing vertebra levels and utilizing differing phases of contrast enhancement. Furthermore, fat measurements have been extracted from CT using different Hounsfield Unit (HU) ranges. To our knowledge, there have been no large studies of healthy cohorts that reported reference values for visceral fat area and radiation attenuation at multiple vertebra levels, for different contrast phases, and using different fat HU ranges. Two-phase CT scans from 1,677 healthy, adult kidney donors (age 18–65) between 1999 and 2017, previously studied to determine healthy reference values for skeletal muscle measures, were utilized. Visceral adipose tissue cross-sectional area (VFA) and radiation attenuation (VFRA) measures were quantified using axial slices at T10 through L4 vertebra levels. T-tests were used to compare males and females, while paired t-tests were conducted to determine the effect (magnitude and direction) of (a) contrast enhancement and (b) different fat HU ranges on each fat measure at each vertebra level. We report the means, standard deviations, and effect sizes of contrast enhancement and fat HU range. Male and female VFA and VFRA were significantly different at all vertebra levels in both contrast and non-contrast scans. Peak VFA was observed at L4 in females and L2 in males, while peak VFRA was observed at L1 in both females and males. In general, non-contrast scans showed significantly greater VFA and VFRA compared to contrast scans. The average paired difference due to contrast ranged from 1.6 to − 8% (VFA) and 3.2 to − 3.0% (VFRA) of the non-contrast value. HU range showed much greater differences in VFA and VFRA than contrast. The average paired differences due to HU range ranged from − 5.3 to 22.2% (VFA) and − 5.9 to 13.6% (VFRA) in non-contrast scans, and − 4.4 to 20.2% (VFA) and − 4.1 to 12.6% (VFRA) in contrast scans. The − 190 to − 30 HU range showed the largest differences in both VFA (10.8% to 22.2%) and VFRA (7.6% to 13.6%) compared to the reference range (− 205 to − 51 HU). Incidentally, we found that differences in lung inflation result in very large differences in visceral fat measures, particularly in the thoracic region. We assessed the independent effects of contrast presence and fat HU ranges on visceral fat cross-sectional area and mean radiation attenuation, finding significant differences particularly between different fat HU ranges. These results demonstrate that CT measurements of visceral fat area and radiation attenuation are strongly dependent upon contrast presence, fat HU range, sex, breath cycle, and vertebra level of measurement. We quantified contrast and non-contrast reference values separately for males and females, using different fat HU ranges, for lumbar and thoracic CT visceral fat measures at multiple vertebra levels in a healthy adult US population.

Metabolic Regulation of Inflammation and Its Resolution: Current Status, Clinical Needs, Challenges, and Opportunities

Metabolism and inflammation have been viewed as two separate processes with distinct but critical functions for our survival: metabolism regulates the utilization of nutrients, and inflammation is responsible for defense and repair. Both respond to an organism's stressors to restore homeostasis. The interplay between metabolic status and immune response (immunometabolism) plays an important role in maintaining health or promoting disease development. Understanding these interactions is critical in developing tools for facilitating novel preventative and therapeutic approaches for diseases, including cancer. This trans-National Institutes of Health workshop brought together basic scientists, technology developers, and clinicians to discuss state-of-the-art, innovative approaches, challenges, and opportunities to understand and harness immunometabolism in modulating inflammation and its resolution.

New Insights on the Relationship between Leptin, Ghrelin, and Leptin/Ghrelin Ratio Enforced by Body Mass Index in Obesity and Diabetes

Currently, adipose tissue is considered an endocrine organ, however, there are still many questions regarding the roles of adipokines—leptin and ghrelin being two adipokines. The purpose of the study was to assess the relationship between the adipokines and their ratio with obesity and diabetes. Methods: Sixty patients (mean age 61.88 ± 10.08) were evaluated. Cardiovascular risk factors, leptin, ghrelin, and insulin resistance score values were assessed. The patients were classified according to their body mass index (BMI) as normal weight, overweight, and obese. Results: 20% normal weight, 51.7% overweight, 28.3% obese, and 23.3% diabetic. Obese patients had higher leptin values (in obese 34,360 pg/mL vs. overweight 18,000 pg/mL vs. normal weight 14,350 pg/mL, p = 0.0049) and leptin/ghrelin ratio (1055 ± 641 vs. 771.36 ± 921 vs. 370.7 ± 257, p = 0.0228). Stratifying the analyses according to the presence of obesity and patients’ gender, differences were found for leptin (p = 0.0020 in women, p = 0.0055 in men) and leptin/ghrelin ratio (p = 0.048 in women, p = 0.004 in men). Mean leptin/BMI and leptin/ghrelin/BMI ratios were significantly higher, and the ghrelin/BMI ratio was significantly lower in obese and diabetic patients. In conclusion, obesity and diabetes are associated with changes not only in the total amount but also in the level of adipokines/kg/m². Changes appear even in overweight subjects, offering a basis for early intervention in diabetic and obese patients.

Artificial intelligence and abdominal adipose tissue analysis: a literature review

Body composition imaging relies on assessment of tissues composition and distribution. Quantitative data provided by body composition imaging analysis have been linked to pathogenesis, risk, and clinical outcomes of a wide spectrum of diseases, including cardiovascular and oncologic. Manual segmentation of imaging data allows to obtain information on abdominal adipose tissue; however, this procedure can be cumbersome and time-consuming. On the other hand, quantitative imaging analysis based on artificial intelligence (AI) has been proposed as a fast and reliable automatic technique for segmentation of abdominal adipose tissue compartments, possibly improving the current standard of care. AI holds the potential to extract quantitative data from computed tomography (CT) and magnetic resonance (MR) images, which in most of the cases are acquired for other purposes. This information is of great importance for physicians dealing with a wide spectrum of diseases, including cardiovascular and oncologic, for the assessment of risk, pathogenesis, clinical outcomes, response to treatments, and complications. In this review we summarize the available evidence on AI algorithms aimed to the segmentation of visceral and subcutaneous adipose tissue compartments on CT and MR images.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard

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.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ^©RSNA, 2021.

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.