Publication Name	World Journal of Gastroenterology
Manuscript ID	107197
Country	Japan

Received	2025-03-19 11:23
Peer-Review Started	2025-03-19 11:23
To Make the First Decision
Return for Revision	2025-04-08 07:12
Revised	2025-04-11 22:58
Second Decision	2025-04-23 02:39
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-23 05:36
Articles in Press	2025-04-23 05:36
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-05-30 03:36
Publish the Manuscript Online	2025-06-12 03:45

ISSN	1007-9327 (print) and 2219-2840 (online)
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/
Copyright	© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
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Website	http://www.wjgnet.com

Category	Oncology
Manuscript Type	Letter to the Editor
Article Title	Clinical implications of a machine learning model predicting colorectal polyp recurrence after endoscopic mucosal resection
Manuscript Source	Unsolicited Manuscript
All Author List	Yoshinori Kagawa
ORCID	Author(s) ORCID Number Yoshinori Kagawa http://orcid.org/0000-0001-6876-4507
Funding Agency and Grant Number
Corresponding Author	Yoshinori Kagawa, Chief Physician, MD, PhD, Department of Gastroenterological Surgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan. yoshinori.kagawa@oici.jp
Key Words	Colorectal polyps; Machine learning; Risk prediction; Endoscopic mucosal resection; Precision medicine
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.
Publish Date	2025-06-12 03:45
Citation	<p>Kagawa Y. Clinical implications of a machine learning model predicting colorectal polyp recurrence after endoscopic mucosal resection. <i>World J Gastroenterol</i> 2025; 31(22): 107197</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i22/107197.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i22.107197

Full Article (PDF)	WJG-31-107197-with-cover.pdf
Manuscript File	107197_Auto_Edited_065650.docx
Answering Reviewers	107197-answering-reviewers.pdf
Audio Core Tip	107197-audio.mp3
Conflict-of-Interest Disclosure Form	107197-conflict-of-interest-statement.pdf
Copyright License Agreement	107197-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	107197-non-native-speakers.pdf
Peer-review Report	107197-peer-reviews.pdf
Scientific Misconduct Check	107197-scientific-misconduct-check.png
Scientific Editor Work List	107197-scientific-editor-work-list.pdf
CrossCheck Report	107197-crosscheck-report.pdf