Publication Name	World Journal of Gastrointestinal Surgery
Manuscript ID	119087
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Opinion Review
Article Title	Systematic assessment of mixed imputation methods and explainable machine learning
Manuscript Source	Invited Manuscript
All Author List	Jia-Yi Li and Yan Zhao
Funding Agency and Grant Number
Corresponding Author	Yan Zhao, Chief Physician, Dean, Director, MD, PhD, Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology, No. 44 Xiaoheyan Street, Dadong District, Shenyang 110042, Liaoning Province, China. drzhao@dlut.edu.cn
Key Words	Gastric cancer; Machine learning; Survival prediction; Missing data imputation; Extra trees
Core Tip	This opinion review evaluates the study by Lü et al, which introduces a hybrid imputation framework, HDI-MF-Gower, combined with an explainable extra trees classifier for predicting postoperative survival in gastric cancer. The study’s innovative approach addresses the challenges of missing clinical data by adapting the iterative imputation method MissForest. Although the model demonstrates clinical transparency and methodological rigor, its marginal improvement over existing ensemble methods and lack of external validation highlight areas for further research in multimodal data integration and multi-institutional validation.
Citation	Li JY, Zhao Y. Systematic assessment of mixed imputation methods and explainable machine learning. World J Gastrointest Surg 2026; In press

Received	2026-01-19 01:49
Peer-Review Started	2026-01-19 01:51
First Decision by Editorial Office Director	2026-01-29 08:06
Return for Revision	2026-01-29 08:06
Revised	2026-02-01 10:25
Publication Fee Transferred
Second Decision by Editor	2026-04-14 02:38
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-04-14 08:34
Articles in Press	2026-04-14 08:34
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-9366 (online)
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