Li GY, Zhai LL. Insights into a machine learning-based prediction model for colorectal polyp recurrence after endoscopic mucosal resection. World J Gastroenterol 2025; 31(31): 109389 [DOI: 10.3748/wjg.v31.i31.109389]
Corresponding Author of This Article
Lu-Lu Zhai, MD, Department of General Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17 Lujiang Road, Hefei 230001, Anhui Province, China. jackyzhai123@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Aug 21, 2025; 31(31): 109389 Published online Aug 21, 2025. doi: 10.3748/wjg.v31.i31.109389
Insights into a machine learning-based prediction model for colorectal polyp recurrence after endoscopic mucosal resection
Guang-Yao Li, Lu-Lu Zhai
Guang-Yao Li, Department of General Surgery, The Second People’s Hospital of Wuhu, Wuhu 241000, Anhui Province, China
Lu-Lu Zhai, Department of General Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
Author contributions: Li GY wrote the original draft; Zhai LL contributed to conceptualization, writing, reviewing and editing; and all authors have read and approved the final version of the manuscript.
Supported by the Wuhu Municipal Science and Technology Bureau Project, No. 2024kj072.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Lu-Lu Zhai, MD, Department of General Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 17 Lujiang Road, Hefei 230001, Anhui Province, China. jackyzhai123@163.com
Received: May 9, 2025 Revised: May 22, 2025 Accepted: July 25, 2025 Published online: August 21, 2025 Processing time: 101 Days and 19 Hours
Abstract
In 2025, Shi et al constructed a model utilizing machine learning techniques to predict the one-year recurrence of colorectal polyps following endoscopic mucosal resection, showing excellent discriminatory performance with an area under the curve exceeding 0.90. However, limitations exist regarding its narrow temporal scope, potential overestimation due to feature collinearity and imputation opacity, and limited generalizability due to single-center derivation and validation. Moreover, no clear clinical implementation strategy was outlined. Prospective multicenter validation and integration of endoscopist variability, longitudinal outcome data, and deployment mechanisms are warranted to ensure broader applicability and clinical utility.
Core Tip: This letter provides a critical appraisal of a recent machine learning model designed to predict colorectal polyp recurrence after endoscopic mucosal resection. It highlights key methodological issues, such as endpoint selection, imputation transparency, and external validation, while offering constructive recommendations to enhance clinical applicability and alignment with international surveillance guidelines.
