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Articles Published Processes
5/21/2025 5:53:54 AM | Browse: 126 | Download: 489
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Received |
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2025-03-04 07:42 |
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Peer-Review Started |
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2025-03-04 07:42 |
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First Decision by Editorial Office Director |
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2025-03-26 02:35 |
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Return for Revision |
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2025-03-26 02:35 |
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Revised |
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2025-04-06 10:21 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-05-06 02:57 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-05-06 05:53 |
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Articles in Press |
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2025-05-06 05:53 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-05-13 08:46 |
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Publish the Manuscript Online |
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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. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Editorial |
| Article Title |
Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Vasily Isakov |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Ministry of Science and Higher Education of the Russian Federation |
FGMF-2025-0003 |
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| 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 |
Isakov V. Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up. World J Gastroenterol 2025; 31(19): 106628 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i19/106628.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i19.106628 |
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