Development and validation of a machine learning-based prognostic model for gastric cancer: a multicenter retrospective study.

BACKGROUND

Machine learning has emerged as a promising tool for survival prediction in various diseases; however, its application and external validation in real-world gastric cancer populations remain limited.

METHODS

Clinical data of patients diagnosed with gastric cancer between 2000 and 2018 were obtained from the SEER database, supplemented with data from two Chinese medical centers (2005-2018). Three feature selection methods and four modeling algorithms-including Cox, RSF, CoxBoost, and Deepsurv_Cox-were employed to construct prediction models for overall survival (OS) and cancer-specific survival (CSS). Model performance was evaluated using the concordance index (C-index), integrated Brier score (IBS), and mean area under the curve (AUC). The two best-performing base models were subsequently integrated into a stacked model and compared against the traditional TNM staging system using decision curve analysis (DCA) and time-dependent ROC curves at 3, 5, and 10 years.

RESULTS

A total of 21,559 patients from the SEER database and 3,805 patients from two Chinese centers were included. In independent testing, the integrated model achieved a C-index/IBS/mean AUC of 0.693/0.158/0.829 for OS and 0.719/0.171/0.819 for CSS. For 3-, 5-, and 10-year survival prediction, the AUCs were 0.705/0.747/0.851 for OS and 0.734/0.779/0.830 for CSS, outperforming the TNM staging system across all metrics. Superior calibration and clinical utility of the integrated model were further confirmed by calibration curves and DCA.

CONCLUSION

The integrated machine learning model outperformed both traditional TNM staging and deep learning approaches, offering improved predictive accuracy for survival outcomes in patients with gastric cancer.