Predicting gallstones risk with body composition analysis and machine learning: a dual-center cohort study

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.

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 into gallstone group and non‑gallstone group by follow‑up findings. A deep-learning tool, Body and Organ Analysis (BOA), was used to quantify fat, muscle, and bone at the level of the third lumbar vertebra. The area under the receiver operating characteristic curve (AUC) of these indices was compared with that of anthropometric and laboratory indices. Predictive models were developed in the training cohort. Model performance was evaluated using fivefold cross-validation and an independent test cohort.

1,944 patients were evaluated, including 1,437 in the training cohort (Center 1; median age, 63 years [25th-75th percentile, 55-72]; 699 males) and 507 in the test cohort (Center 2; median age, 63 years [55-71]; 266 males). In univariate analysis, neutrophil-to-lymphocyte ratio (NLR) showed the highest AUC (0.627, 95% confidence interval [CI]: 0.586-0.668). The extreme trees (ET) model performed best, with a test-set AUC of 0.772 (95% CI: 0.714-0.822). SHapley Additive exPlanations (SHAP) analysis identified the area ratio of subcutaneous to total fat as the most important feature.

NLR was the best single predictor but had limited standalone utility. Among models integrating the three indicator categories, the ET performed best.