ArticleWorld journal of gastroenterology2025
Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Observational
- Clinical implications of a dynamic nomogram for predicting sepsis in acute liver failure.World journal of gastroenterology · 2026Article
- Development and Internal Validation of a LASSO-Based Prediction Model for Colorectal Adenoma Recurrence After Polypectomy.Cancer management and research · 2026Article
- Development and validation of a machine learning model for predicting 3-year recurrence of adenomatous colorectal polyps after EMR: a multicenter study.Frontiers in oncology · 2026Article
- Impact of gut microbiome on outcomes following endoscopic interventions in gastrointestinal disease.World journal of gastrointestinal endoscopy · 2025Article
- Machine Learning-Based Prediction of Histopathological Classification in Colorectal Polyps.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Insights into a machine learning-based prediction model for colorectal polyp recurrence after endoscopic mucosal resection.World journal of gastroenterology · 2025Article
- Prevention and treatment of recurrence after endoscopic resection of large non-pedunculated colorectal polyps.World journal of gastrointestinal endoscopy · 2025Review
- Clinical implications of a machine learning model predicting colorectal polyp recurrence after endoscopic mucosal resection.World journal of gastroenterology · 2025Article
- Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy?World journal of gastroenterology · 2025Article
- Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.World journal of gastroenterology · 2025Article
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Authors and funding
8 authors.
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Abstract
backgroundColorectal 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 procedure 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.
aimTo construct and validate a machine learning (ML) model for predicting the risk of colorectal polyp recurrence one year after EMR.
methodsThis 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 univariate 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.
resultsMultivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR (
conclusionThe XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.
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