Predicting gastric cancer survival using machine learning: A systematic review.

[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.