Publication Name	World Journal of Gastrointestinal Surgery
Manuscript ID	114951
Country	China

Category	Computer Science, Interdisciplinary Applications
Manuscript Type	Retrospective Study
Article Title	Development of a machine learning-based model for predicting postoperative survival in gastric cancer
Manuscript Source	Unsolicited Manuscript
All Author List	Ya-Na Lü, Dong Liu, Shuai Tao, Ju Wu, Shu-Juan Yu and Hui-Ling Yuan
Funding Agency and Grant Number
Corresponding Author	Ju Wu, Department of General Surgery, Zhongshan Hospital Affiliated to Dalian University, No 6 Jiefang Street, Zhongshan District, Dalian 116001, Liaoning Province, China. wuju@s.dlu.edu.cn
Key Words	Gastric cancer; Machine learning; Survival prediction; Missing data imputation; Extra trees
Core Tip	This study developed a novel hybrid imputation method (HDI-MF-Gower) to handle missing clinical data. We then built and validated a robust machine learning model (extra trees classifier) for predicting postoperative 3-year survival in gastric cancer patients. The model demonstrated high performance (area under the curve of 0.853), and its clinical application is facilitated by interpretable SHapley Additive exPlanations analysis and a user-friendly online prediction tool, aiding personalized treatment planning.
Citation	Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; In press

Received	2025-10-11 05:35
Peer-Review Started	2025-10-11 05:37
First Decision by Editorial Office Director	2025-11-19 09:59
Return for Revision	2025-11-19 09:59
Revised	2025-12-02 12:50
Publication Fee Transferred	2025-12-09 02:30
Second Decision by Editor	2026-01-08 02:47
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-08 07:26
Articles in Press	2026-01-08 07:26
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-9366 (online)
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