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Articles Published Processes
1/12/2026 10:07:35 AM | Browse: 1 | Download: 11
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Received |
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2025-09-22 02:40 |
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Peer-Review Started |
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2025-09-22 02:40 |
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First Decision by Editorial Office Director |
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2025-09-25 07:39 |
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Return for Revision |
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2025-09-25 07:39 |
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Revised |
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2025-10-07 08:13 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-11-06 02:48 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-11-06 09:05 |
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Articles in Press |
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2025-11-06 09:05 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-01-04 02:11 |
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Publish the Manuscript Online |
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2026-01-12 10:07 |
| ISSN |
1948-5204 (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: 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 |
Letter to the Editor |
| Article Title |
Machine learning approaches to early detection of delayed wound healing following gastric cancer surgery
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Duygu Kirkik, Huseyin Murat Ozadenc and Sevgi Kalkanli Tas |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Duygu Kirkik, Assistant Professor, Department of Immunology, Hamidiye Medicine Faculty, University of Health Sciences, Mekteb-i Tıbbiyye-i Sahane (Haydarpasa) Kulliyesi Selimiye Mah Tıbbiye Cad No. 38, Istanbul 34668, Türkiye. dygkirkik@gmail.com |
| Key Words |
Gastric cancer; Radical gastrectomy; Delayed wound healing; Machine learning; Decision tree; Risk prediction |
| Core Tip |
This letter highlights the potential of machine learning models in predicting delayed wound healing after radical gastrectomy. By leveraging routinely available clinical and laboratory parameters, interpretable models such as decision trees may support early risk stratification and postoperative monitoring. However, external validation and the use of pre- or intraoperative variables are essential before widespread clinical adoption. |
| Publish Date |
2026-01-12 10:07 |
| Citation |
Kirkik D, Ozadenc HM, Kalkanli Tas S. Machine learning approaches to early detection of delayed wound healing following gastric cancer surgery. World J Gastrointest Oncol 2026; 18(1): 114499 |
| URL |
https://www.wjgnet.com/1948-5204/full/v18/i1/114499.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v18.i1.114499 |
© 2004-2026 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
California Corporate Number: 3537345
