Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection  

作　　者：Yi-Heng Shi Jun-Liang Liu Cong-Cong Cheng Wen-Ling Li Han Sun Xi-Liang Zhou Hong Wei Su-Juan Fei 

机构地区：[1]Department of Gastroenterology,The Affiliated Hospital of Xuzhou Medical University,Xuzhou 221002,Jiangsu Province,China [2]The First Clinical Medical College of Xuzhou Medical University,Xuzhou 221002,Jiangsu Province,China [3]Department of Gastroenterology,Xuzhou Central Hospital,The Affiliated Xuzhou Hospital of Medical College of Southeast University,Xuzhou 221009,Jiangsu Province,China [4]Department of Gastroenterology,Xuzhou New Health Hospital,North Hospital of Xuzhou Cancer Hospital,Xuzhou 221007,Jiangsu Province,China

出　　处：《World Journal of Gastroenterology》2025年第11期46-62,共17页世界胃肠病学杂志(英文)

摘　　要：BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.

关 键 词：Colorectal polyps Machine learning Predictive model Risk factors SHapley Additive exPlanation 

分 类 号：R73[医药卫生—肿瘤]