Publication Name	World Journal of Gastroenterology
Manuscript ID	106628
Country	Russia

Received	2025-03-04 07:42
Peer-Review Started	2025-03-04 07:42
To Make the First Decision
Return for Revision	2025-03-26 02:35
Revised	2025-04-06 10:21
Second Decision	2025-05-06 02:57
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-05-06 05:53
Articles in Press	2025-05-06 05:53
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-05-13 08:46
Publish the Manuscript Online	2025-05-21 05:53

ISSN	1007-9327 (print) and 2219-2840 (online)
Open Access	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: http://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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Category	Gastroenterology & Hepatology
Manuscript Type	Editorial
Article Title	Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up
Manuscript Source	Invited Manuscript
All Author List	Vasily Isakov
ORCID	Author(s) ORCID Number Vasily Isakov http://orcid.org/0000-0002-4417-8076
Funding Agency and Grant Number	Funding Agency Grant Number Ministry of Science and Higher Education of the Russian Federation FGMF-2025-0003
Corresponding Author	Vasily Isakov, MD, PhD, Professor, Department of Gastroenterology and Hepatology, Federal Research Center of Nutrition, Biotechnology and Food Safety, 21 Kashirskoye Shosse, Moscow 115446, Russia. vasily.isakov@gmail.com
Key Words	Colorectal polyps; Endoscopic mucosal resection; Colorectal polyp recurrence; Machine learning; Artificial Intelligence; Recurrence risk assessment; Surveillance strategies
Core Tip	The recurrence rates of colorectal polyps after endoscopic mucosal resection remain high. Traditional surveillance strategies rely only on polyp characteristics, potentially missing important risk factors. Machine learning-based models leveraging patient- and polyp-related factors may accurately predict polyp recurrence. Personalized machine-learning-driven risk stratification may optimize surveillance, reduce unnecessary procedures, and improve early cancer detection and cost-effectiveness. Future models should be validated across diverse populations.
Publish Date	2025-05-21 05:53
Citation	<p>Isakov V. Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up. <i>World J Gastroenterol</i> 2025; 31(19): 106628</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i19/106628.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i19.106628

Full Article (PDF)	WJG-31-106628-with-cover.pdf
Manuscript File	106628_Auto_Edited_064323.docx
Answering Reviewers	106628-answering-reviewers.pdf
Audio Core Tip	106628-audio.m4a
Conflict-of-Interest Disclosure Form	106628-conflict-of-interest-statement.pdf
Copyright License Agreement	106628-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	106628-foundation-statement.pdf
Non-Native Speakers of English Editing Certificate	106628-non-native-speakers.pdf
Peer-review Report	106628-peer-reviews.pdf
Scientific Misconduct Check	106628-scientific-misconduct-check.png
Scientific Editor Work List	106628-scientific-editor-work-list.pdf
CrossCheck Report	106628-crosscheck-report.pdf