Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	114499
Country	Türkiye

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
Manuscript Source	Invited Manuscript
All Author List	Duygu Kirkik, Huseyin Murat Ozadenc and Sevgi Kalkanli Tas
Funding Agency and Grant Number
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.
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 2025; In press

Received	2025-09-22 02:40
Peer-Review Started	2025-09-22 02:40
To Make the First Decision
Return for Revision	2025-09-25 07:39
Revised	2025-10-07 08:13
Second Decision	2025-11-06 02:48
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-11-06 09:05
Articles in Press	2025-11-06 09:05
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5204 (online)
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