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. Early postoperative recurrence is a major cause of treatment failure in patients with locally advanced gastric cancer (LAGC). Here, the authors develop Recurrence Stratification and Assessment (RSA), a deep learning model that integrates histopathology with clinical variables to predict postoperative recurrence in LAGC; they also examine the influence of immunological activity in recurrence.