Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and
Alan Ranieri Guimaraes1, Steven E Williams1,2, Mark T Macmillan1,3
1British Heart Foundation Centre for Research Excellence, Institute for Neuroscience and Cardiovascular Research, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, United Kingdom.
Radiology
|June 30, 2026
Summary
Machine learning analysis of coronary CT angiography reveals body composition impacts 10-year outcomes. Lower skeletal muscle attenuation is linked to increased mortality and myocardial infarction risk.
Area of Science:
- Radiology
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
Background:
- Coronary CT angiography (CCTA) offers prognostic insights beyond coronary findings.
- Machine learning (ML) can derive detailed body composition from CCTA images.
- Understanding multiorgan composition's role in cardiovascular outcomes is crucial.
Purpose of the Study:
- To assess associations between ML-derived multiorgan body composition and 10-year outcomes.
- To investigate the prognostic value of body composition in the SCOT-HEART trial.
- To identify specific body composition metrics related to mortality and myocardial infarction (MI).
Main Methods:
- Retrospective analysis of 1722 patients from the SCOT-HEART trial using wide field-of-view CCTA images.
- Application of the TotalSegmentator model for automated organ segmentation and calculation of volume and mean attenuation.
- Construction of multivariable Cox proportional hazards models adjusted for age, sex, and scan length to predict all-cause mortality and MI.
Main Results:
- Higher lung attenuation, lower liver attenuation, and greater torso fat volume were associated with coronary artery disease.
- Increased skeletal muscle attenuation correlated with reduced all-cause mortality (HR, 0.61).
- Lower skeletal muscle attenuation was independently associated with increased risk of mortality (HR, 1.85) and MI (HR, 1.58) after adjustment for coronary calcium score.
Conclusions:
- ML-driven multiorgan body composition analysis from CCTA provides significant prognostic information.
- Skeletal muscle attenuation emerged as a particularly strong predictor of adverse cardiovascular outcomes.
- CCTA-derived body composition can enhance risk stratification beyond traditional coronary findings.
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