An artificial intelligence model to predict survival and chemotherapy benefits for gastric cancer patients after gastrectomy development and validation in international multicenter cohorts

Abstract

BACKGROUND: Gastric cancer (GC) is a major health problem worldwide, with high prevalence and mortality. The present GC staging system provides inadequate prognostic information and does not reflect the chemotherapy benefit of GC. METHODS: Two hundred fifty-five patients who underwent surgical resection were enrolled in our study (training cohort = 212, internal validation cohort = 43). Nine clinicopathologic features were obtained to construct an support vector machine (SVM) model. The cohorts from 4 domestic centres and The Cancer Genome Atlas (TCGA) were used for external validation. RESULTS: In the training cohort, the AUCs were 0.773 (95% CI 0.708-0.838) for 5-year overall survival (OS) and 0.751 (95% CI 0.683-0.820) for 5-year disease-free survival (DFS); in the domestic validation cohort, the AUCs were 0.852 (95% CI 0.810-0.894) and 0.837 (95% CI 0.792-0.882), respectively. The model performed better than the TNM staging system according to the receiver operator characteristic(ROC) curve. GC patients were significantly divided into low, moderate and high risk based on the SVM. High-risk TNM stage Ⅱ and Ⅲ patients were more likely to benefit from adjuvant chemotherapy than low-risk patients. CONCLUSIONS: The SVM-based model may be used to predict OS and DFS in GC patients and the benefit of adjuvant chemotherapy in TNM stage Ⅱ and Ⅲ GC patients.