Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and Mortality in the SCOT-HEART Trial.
A, R.G., SE, W., MT, M., K, W., C, W., JR, W.M., PD, A., G, R., NL, M., & MR, D. (2026). Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and Mortality in the SCOT-HEART Trial.. Radiology. https://doi.org/10.1148/radiol.251821
A RG, SE W, MT M, K W, C W, JR WM, et al. Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and Mortality in the SCOT-HEART Trial.. Radiology. 2026; doi: 10.1148/radiol.251821
A RG, SE W, MT M, et al. Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and Mortality in the SCOT-HEART Trial.[J]. Radiology. 2026. DOI: 10.1148/radiol.251821.
@article{a2026,
author = {Ranieri Guimaraes A and Williams SE and Macmillan MT and Wood K and Wang C and Weir-McCall JR and Adamson PD and Roditi G and Mills NL and Dweck MR},
title = {Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and Mortality in the SCOT-HEART Trial.},
journal = {Radiology},
year = {2026},
doi = {10.1148/radiol.251821},
note = {PMID: 42377128},
}
TY - JOUR AU - Ranieri Guimaraes A AU - Williams SE AU - Macmillan MT AU - Wood K AU - Wang C AU - Weir-McCall JR AU - Adamson PD AU - Roditi G AU - Mills NL AU - Dweck MR TI - Machine Learning Multiorgan Analysis of Coronary CT Angiography Body Composition, Myocardial Infarction, and Mortality in the SCOT-HEART Trial. T2 - Radiology PY - 2026 DO - 10.1148/radiol.251821 AN - PMID:42377128 ER -
Background Coronary CT angiography provides prognostic information in addition to coronary findings. Purpose To evaluate associations between machine learning-derived multiorgan body composition and 10-year outcomes in the SCOT-HEART (Scottish Computed Tomography of the Heart) trial. Materials and Methods Wide field-of-view images of 1722 patients (recruited between November 2010 and September 2014) were retrospectively processed using the TotalSegmentator model. The volume and mean attenuation of segmented organs were calculated. Multivariable Cox proportional hazards models were constructed for all-cause mortality and myocardial infarction (MI), adjusted for age, sex, and scan length. Odds ratios or hazard ratios (HRs) and 95% CIs were calculated per 10-unit increase in attenuation or volume. Results Mortality and MI occurred in 133 (7.72%) and 106 (6.16%) of the 1722 patients, respectively (age, 57.5 years ± 9.5 [SD]; 55.7% male). Coronary artery disease was associated with greater lung attenuation (odds ratio, 1.04 [95% CI: 1.03, 1.06]; P < .001), lower liver attenuation (odds ratio, 0.87 [95% CI: 0.8, 0.95]; P = .034), and greater torso fat volume (odds ratio, 1.01 [95% CI: 1.01, 1.02]; P < .001) after multivariable adjustment. Increased skeletal muscle attenuation was associated with lower all-cause mortality (HR, 0.61 [95% CI: 0.47, 0.79]; P < .001) after multivariable adjustment. MI was associated with increased myocardial volume (HR, 1.09 [95% CI: 1.01, 1.16]; P = .018) and decreased rib (HR, 0.98 [95% CI: 0.96, 1.0]; P = .043) and skeletal muscle (HR, 0.69 [95% CI: 0.54, 0.87]; P = .002) attenuation after multivariable adjustment. However, when further adjusted for coronary calcium score, only skeletal muscle attenuation was associated with MI (HR, 0.72 [95% CI: 0.57, 0.91]; P = .007). Patients with skeletal muscle attenuation below the median had a higher risk of mortality (HR, 1.85 [95% CI: 1.30, 2.64]; P < .001) or experience MI (HR, 1.58 [95% CI: 1.07, 2.33]; P = .022). Conclusion Multiorgan body composition analysis using coronary CT angiography provided additional prognostic information, among which skeletal muscle attenuation was particularly important. ClinicalTrials.gov identifier: NCT01149590 © RSNA, 2026 Supplemental material is available for this article.