acePredicting gallstones risk with body composition analysis and machine learning: a dual-center cohort study
Background To compare the predictive performance of computed tomography (CT) body composition indices, anthropometric indices, and laboratory indices for gallstones occurrence, and to construct machine-learning models to improve performance. Methods The dual-center retrospective cohort enrolled patients who underwent initial abdominal CT between January 2017 and January 2023, had no gallstones detected, and completed at least 3 years of follow-up. The data analysis was performed in April 2026. They were divided…
Combining CT body composition, anthropometric and laboratory indices in an extreme trees model reached a test-set AUC of 0.772, outperforming the best single predictor at 0.627.
The study was retrospective, and even the best model had a moderate test AUC, while the best single marker had limited standalone utility.
Evidence
- Peer-reviewedAnnals of Medicine2026-10-07
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Truvace Impact Record TRV-2026-1775, v1: “Predicting gallstones risk with body composition analysis and machine learning: a dual-center cohort study.” Truvace, 2026-10-18. /record/TRV-2026-1775 (accessed at citation time). sha256 e5015157e27fc00c…
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