Automated Body Composition from Computed Tomography Scans Improves Survival Prediction in Colorectal Cancer Patients.

Authors

Mushfiqus Salehin, Hyunwoo Lee, Vincent Tze Yang Chow, Erin K Weltzien, Long Nguyen, Jia Ming Li, Varun Akella, Bette J Caan, Elizabeth M Cespedes Feliciano, Da Ma, Mirza Faisal Beg, Karteek Popuri

Published in

Journal of imaging informatics in medicine. Jul 29, 2026. Epub Jul 29, 2026.

Abstract

Colorectal cancer is one of the most common cancers, but the current staging system is limited by the variability and paradoxical survival outcomes. Body composition is an accurate predictor of survival and can be extracted from routine computed tomography (CT) scans used in cancer diagnosis. However, despite its potential, the adoption of body composition analysis has been limited due to the challenges in generating the data. In this study, we propose a deep learning-based model that combines clinical and body composition biomarkers to predict the survival of colorectal cancer patients. Our best model, which integrates both clinical and body composition features, achieved a time-dependent concordance-index score of 0.7298 ( $p<0.001$ ), demonstrating a significant improvement over models based solely on clinical or body composition biomarkers, indicating that models combining body composition and clinical markers could improve survival prediction. Additionally, we observed that increased skeletal muscle tissue area and radiodensity were associated with reduced mortality risk, while higher radiodensities of visceral and subcutaneous adipose tissues were associated with increased risk.

PMID:
42527786
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

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