Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction

Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT

PURPOSE

To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).

METHODS

This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70 keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70 keV data and to estimate their equivalent 70 keV biomarker attenuation values.

RESULTS

Relative to 70 keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100 keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm2 for 40, 55, 85, 100 keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm3 for 40, 55, 85, 100 keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70 keV showed more variation in median biomarker measurement than VMI > 70 keV.

CONCLUSION

This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70 keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams.

Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT

Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the entire process-from data normalization and anatomical landmarking to automated tissue segmentation and quantitative biomarker extraction. Our methodology ensures standardized inputs and robust segmentation models to compute volumetric, density, and cross-sectional area metrics for a range of organs and tissues. Additionally, we capture selected DICOM header fields to enable downstream analysis of scan parameters and facilitate correction for acquisition-related variability. By emphasizing portability and compatibility across different scanner types, image protocols, and computational environments, we ensure broad applicability of our framework. This toolkit is the basis for the Opportunistic Screening Consortium in Abdominal Radiology (OSCAR) and has been shown to be robust and versatile, critical for large multi-center studies.

Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes

OBJECTIVE

To quantify the potential of fully automated CT-based body composition metrics and clinical frailty data in predicting liver transplant recipient postoperative outcomes.

METHODS

AI-enabled body composition tools were applied to pre-transplant abdominal CT scans in a retrospective cohort of first-time deceased-donor liver transplant recipients. Clinical frailty data (Fried frailty score) was obtained from an established transplant database. Age- and sex-corrected hazard ratios (HRs) were analyzed according to highest-risk quartiles compared with the other three quartiles combined. Area under the receiver operating characteristic curve (ROC AUC) analysis in univariate and multivariate scenarios was also performed.

RESULTS

598 liver transplant recipients (median age, 56 years [IQR, 49-61]; 383 men/215 women) were included from 2005 to 2021. Mean clinical follow-up interval after transplant was 8.6 ± 4.5 years, with 224 deaths (mean interval, 5.3 ± 3.9 years post-transplant) and 246 graft failures (mean interval, 4.7 ± 4.0 years post-transplant) observed. Univariate HRs for post-transplant survival included 1.53 (95% CI, 1.14-2.06) for muscle attenuation, 1.66 (95% Cl, 1.24-2.22) for aortic Agatston score, 1.35 (1.02-1.80) for SAT area, and 1.82 (1.35-2.46) for liver volume. For those meeting the frailty criteria, HR was 2.14 (1.08-4.22). Multivariate 10-year AUC for predicting mortality was 0.675 using liver volume, aortic Agatston score, and muscle attenuation. 10-year univariate AUC for clinical frailty assessment was 0.601 but increased to 0.878 when combined with CT measures.

CONCLUSION

Automated CT measurements of muscle density (myosteatosis), aortic calcification, subcutaneous fat, and liver volume are predictive of mortality in liver transplant recipients. Frailty was likewise predictive. Combining CT and clinical frailty assessment was complementary.

KEY POINTS

Question What is the prognostic value of pre-transplant CT-based body composition measures for deceased-donor liver transplant outcomes, and how do they correlate with frailty assessment? Findings Increased post-transplant mortality was associated with pre-transplant increased liver volume, increased abdominal aortic Agatston score, decreased skeletal muscle attenuation, and decreased subcutaneous adipose tissue area. Clinical relevance Pre-transplant AI-enabled body composition measures have predictive value for post-transplant survival, offering a novel and objective diagnostic tool to identify high-risk transplant recipients that are complementary to clinical assessments.

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.

Epicardial adipose tissue volume highly correlates with left ventricular diastolic dysfunction in endogenous Cushing's syndrome

BACKGROUND

Cushing's syndrome (CS) is associated with increased risk for heart failure, which often initially manifests as left ventricular diastolic dysfunction (LVDD). In this study, we aimed to explore the potential risk factors of LVDD in CS by incorporating body composition parameters.

METHODS

A retrospective study was conducted on patients diagnosed with endogenous CS no less than 18 years old. The control group consisted of healthy individuals who were matched to CS patients in terms of gender, age, and BMI. LIFEx software (version 7.3) was applied to measure epicardial adipose tissue volume (EATV) on non-contrast chest CT, as well as abdominal adipose tissue and skeletal muscle mass at the first lumbar vertebral level. Echocardiography was used to evaluate left ventricular (LV) diastolic function. Body compositions and clinical data were examined in relation to early LVDD.

RESULTS

A total of 86 CS patients and 86 healthy controls were enrolled. EATV was significantly higher in CS patients compared to control subjects (150.33 cm3 [125.67, 189.41] vs 90.55 cm3 [66.80, 119.84], p < 0.001). CS patients had noticeably increased visceral fat but decreased skeletal muscle in comparison to their healthy counterparts. Higher prevalence of LVDD was found in CS patients based on LV diastolic function evaluated by E/A ratio (p < 0.001). EATV was proved to be an independent risk factor for LVDD in CS patients (OR = 1.015, 95%CI 1.003-1.026, p = 0.011). If the cut-point of EATV was set as 139.252 cm3 in CS patients, the diagnostic sensitivity and specificity of LVDD were 84.00% and 55.60%, respectively.

CONCLUSION

CS was associated with marked accumulation of EAT and visceral fat, reduced skeletal muscle mass, and increased prevalence of LVDD. EATV was an independent risk factor for LVDD, suggesting the potential role of EAT in the development of LVDD in CS.

Medical imaging in cancer cachexia

Cancer cachexia, often referred to as "wasting syndrome," is characterized by fatigue, weakness, and involuntary weight loss. This syndrome is concomitant with progressive skeletal muscle atrophy with or without adipose tissue loss and is frequently accompanied by systemic inflammation. Understanding the complexities of cancer cachexia is crucial for early detection and intervention, and it is also paramount for enhancing patient outcomes. Medical imaging, comprising diverse imaging modalities, plays a pivotal role in this context, facilitating the diagnosis and surveillance assessment of both the disease extent and the body composition changes that offer valuable information and insights into disease progression. This article provides a comprehensive discourse of the pathophysiological mechanisms and clinical manifestations of cancer cachexia as well as the role of medical imaging in this setting. Particular emphasis is placed on contemporary multidisciplinary and translational research efforts for the development of diagnostic and treatment tools, aiming to mitigate the devastating consequences of cancer cachexia.

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.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data

PURPOSE

To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.

METHODS

In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis.

RESULTS

A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400.

CONCLUSION

Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.