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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jan 15, 2026; 18(1): 114499
Published online Jan 15, 2026. doi: 10.4251/wjgo.v18.i1.114499
Published online Jan 15, 2026. doi: 10.4251/wjgo.v18.i1.114499
Machine learning approaches to early detection of delayed wound healing following gastric cancer surgery
Duygu Kirkik, Huseyin Murat Ozadenc, Sevgi Kalkanli Tas, Department of Immunology, Hamidiye Medicine Faculty, University of Health Sciences, Istanbul 34668, Türkiye
Author contributions: Kirkik D designed the overall concept and outline of the manuscript; Ozadenc HM and Kalkanli Tas S contributed to the discussion and design of the manuscript; all authors contributed to the writing, editing the manuscript, review of literature, and approved the final version to publish.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: 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.
Received: September 22, 2025
Revised: October 7, 2025
Accepted: November 6, 2025
Published online: January 15, 2026
Processing time: 113 Days and 11.1 Hours
Revised: October 7, 2025
Accepted: November 6, 2025
Published online: January 15, 2026
Processing time: 113 Days and 11.1 Hours
Core Tip
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.