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.

MeSH

Neoplasms, Machine Learning, Learning, Stomach Neoplasms