Systematic Reviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. May 15, 2025; 17(5): 103804
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103804
Predicting gastric cancer survival using machine learning: A systematic review
Hong-Niu Wang, Jia-Hao An, Fu-Qiang Wang, Wen-Qing Hu, Liang Zong
Hong-Niu Wang, Fu-Qiang Wang, Wen-Qing Hu, Liang Zong, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, The Affiliated Hospital of Changzhi Medical College, Changzhi 046000, Shanxi Province, China
Hong-Niu Wang, Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
Jia-Hao An, Department of Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
Co-first authors: Hong-Niu Wang and Jia-Hao An.
Author contributions: Wang HN and An JH contributed equally to the preparation of the manuscript; Wang HN designed the review, collected and analyzed the data, and wrote the manuscript; An JH also designed the review, collected and analyzed the data, provided detailed explanations for the figures, and drafted the manuscript; Wang FQ, Hu WQ and Zong L reviewed and revised the manuscript. All authors have read and approved the final version of the manuscript.
Conflict-of-interest statement: The authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: All authors have read the PRISMA 2009 checklist, and the manuscript has been prepared and revised according to the PRISMA 2009 checklist.
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: Liang Zong, MD, PhD, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, The Affiliated Hospital of Changzhi Medical College, No. 502 Changxing Middle Road, Changzhi 046000, Shanxi Province, China. 250537471@qq.com
Received: December 4, 2024
Revised: February 20, 2025
Accepted: February 26, 2025
Published online: May 15, 2025
Processing time: 162 Days and 23.6 Hours
Core Tip

Core Tip: Machine learning offers significant promise for predicting gastric cancer patients' survival, but challenges such as data quality, model interpretability, and generalizability must be addressed. This review highlights the importance of integrating diverse data types, robust data preprocessing, and advanced feature-selection techniques to improve prediction accuracy. While open-access and private datasets each have their advantages, ensuring the timeliness and relevance of data is essential for the development of clinically applicable models.