Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 14, 2025; 31(22): 107197
Published online Jun 14, 2025. doi: 10.3748/wjg.v31.i22.107197
Clinical implications of a machine learning model predicting colorectal polyp recurrence after endoscopic mucosal resection
Yoshinori Kagawa
Yoshinori Kagawa, Department of Gastroenterological Surgery, Osaka International Cancer Institute, Osaka 541-8567, Japan
Author contributions: Kagawa Y wrote the full manuscript of this letter, read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: I have no conflict of interest.
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: Yoshinori Kagawa, MD, PhD, Chief Physician, Department of Gastroenterological Surgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan. yoshinori.kagawa@oici.jp
Received: March 19, 2025
Revised: April 11, 2025
Accepted: April 23, 2025
Published online: June 14, 2025
Processing time: 85 Days and 20.6 Hours
Core Tip

Core Tip: This predictive model notably enhanced clinical decision-making for colorectal polyp surveillance, demonstrating high accuracy and ease of clinical implementation through a user-friendly online risk calculator. Although promising, its real-world utility depends on external validation, clinician training, and integration with the existing clinical guidelines.