Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Core Tip

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.