Machine learning based on body composition radiomics for predicting early recurrence in colorectal cancer: a multicenter study.

Background: Early recurrence (ER) in colorectal cancer (CRC) leads to dismal outcomes. Current pTNM staging fails to capture the host's systemic pathophysiological status. We developed an interpretable machine learning (ML) model based on preoperative CT body composition radiomics to predict ER in CRC. Methods: This multicenter study enrolled 917 patients who underwent radical resection across three independent institutions, and the cohort was partitioned into a training set ( Results: An 11-feature radiomics signature was identified. The Random Forest model demonstrated optimal generalization, yielding AUCs of 0.807, 0.776, and 0.750 in the training and two test sets, respectively. SHAP analysis revealed that IMAT (46.1%) and SM (42.9%) features were primary drivers, and increased SM textural uniformity may reflect adverse muscle quality and possible myosteatosis-related tissue alterations. The individualized radiomics risk score effectively stratified patients, demonstrating significantly divergent recurrence-free and overall survival across all cohorts ( Conclusion: This interpretable ML model may improve ER risk stratification in CRC and provide a quantitative tool for individualized postoperative surveillance.