Wang HN, An JH, Wang FQ, Hu WQ, Zong L. Predicting gastric cancer survival using machine learning: A systematic review. World J Gastrointest Oncol 2025; 17(5): 103804 [DOI: 10.4251/wjgo.v17.i5.103804]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Systematic Reviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
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 15.2 Hours
Abstract
BACKGROUND
Gastric cancer (GC) has a poor prognosis, and the accurate prediction of patient survival remains a significant challenge in oncology. Machine learning (ML) has emerged as a promising tool for survival prediction, though concerns regarding model interpretability, reliance on retrospective data, and variability in performance persist.
AIM
To evaluate ML applications in predicting GC survival and to highlight key limitations in current methods.
METHODS
A comprehensive search of PubMed and Web of Science in November 2024 identified 16 relevant studies published after 2019. The most frequently used ML models were deep learning (37.5%), random forests (37.5%), support vector machines (31.25%), and ensemble methods (18.75%). The dataset sizes varied from 134 to 14177 patients, with nine studies incorporating external validation.
RESULTS
The reported area under the curve values were 0.669–0.980 for overall survival, 0.920–0.960 for cancer-specific survival, and 0.710–0.856 for disease-free survival. These results highlight the potential of ML-based models to improve clinical practice by enabling personalized treatment planning and risk stratification.
CONCLUSION
Despite challenges concerning retrospective studies and a lack of interpretability, ML models show promise; prospective trials and multidimensional data integration are recommended for improving their clinical applicability.
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.
