A deep learning-based digital biopsy for predicting early recurrence in gastric cancer.

Early postoperative recurrence is a major cause of treatment failure in patients with locally advanced gastric cancer (LAGC), yet current staging systems inadequately capture the biological heterogeneity that underlies recurrence risk. Here, we introduce a clinically interpretable multimodal prediction model, Recurrence Stratification and Assessment (RSA), which integrates deep learning-derived histopathological features from routine hematoxylin and eosin slides with conventional clinical variables. The model was developed using a retrospective multicenter cohort (n = 1,763) and rigorously validated across two internal cohorts, two geographically distinct external cohorts, and an exploratory post-hoc analysis of a prospective clinical trial population (NCT01516944), demonstrating robust and generalizable performance (area under the curves ranging from 0.843 to 0.887). Shapley Additive Explanations-based interpretation identifies key histological features contributing to recurrence risk. To explore biological underpinnings, we perform transcriptomic sequencing and immune profiling on tumor specimens, revealing immune-enriched microenvironments and elevated checkpoint gene expression in the RSA-defined low-risk group. These findings suggest differential immunological activity may influence recurrence dynamics. This study demonstrates the application of digital pathology-based artificial intelligence for recurrence risk prediction in LAGC, offering not only a high-performance and biologically informed tool, but also a transparent framework for clinical deployment. The RSA model may support risk-adapted postoperative surveillance and provides a biologically informed framework for exploring the potential utility of immune checkpoint inhibitors.