ArticleFrontiers in oncology2026
Development and validation of a machine learning model for predicting 3-year recurrence of adenomatous colorectal polyps after EMR: a multicenter study.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to identify factors associated with adenoma recurrence within three years after endoscopic mucosal resection (EMR) and to develop an individualized predictive model. Methods: Patients undergoing their first EMR for colorectal polyps at the Affiliated Hospital of Xuzhou Medical University (September 2018-May 2025) were retrospectively included and randomly divided into training and testing cohorts (7:3). Patients from the Third Affiliated Hospital of Xuzhou Medical University served as the external validation cohort. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors. Logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) models were constructed. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). SHapley additive explanations (SHAP) values were applied to interpret variable contributions, and an online prediction tool was developed. Results: A total of 1,454 patients were enrolled, of whom 731 developed adenoma recurrence within three years. LASSO identified ten predictors: infection of Helicobacter pylori (H. pylori), the number of adenomas, age, triglyceride level, body mass index (BMI), diarrhea, smoking history, family history, adenoma size, and fecal occult blood positivity. GBM achieved the highest mean AUC in repeated cross-validation (0.813) and maintained robust discrimination in the testing (AUC = 0.818) and external validation cohorts (AUC = 0.775), with good calibration and clinical utility. SHAP analysis identified H. pylori infection, adenoma number, and age as the leading contributors to model predictions. Conclusion: GBM demonstrated favorable discrimination, calibration, and clinical utility across validation cohorts, supporting its use for individualized risk stratification. The resulting online calculator may further facilitate risk assessment and inform follow-up strategies in clinical practice.
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