Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer

Accurate prediction of early postoperative recurrence in locally advanced gastric cancer (LAGC) remains challenging due to tumor heterogeneity and limitations of traditional clinicopathological factors. This study aims to develop and validate an interpretable multimodal model for precise recurrence prediction. 1580 LAGC patients are enrolled from six Chinese medical centers and a multimodal fusion Risk Stratification Assessment (RSA) model integrating clinical, radiomic, and pathomic data is developed. Model performance is evaluated using internal, external, prospective, and public dataset validations. Transcriptome sequencing is conducted to elucidate biological mechanisms underlying recurrence. The RSA model significantly outperforms clinical-only, radiomic-only, and pathomic-only models in predicting early recurrence, achieving area under the curve (AUC) values of 0.903 in the training cohort, 0.902 in internal validation, and ranging from 0.884 to 0.889 in external validations. Stratification by the RSA model consistently identifies high-risk patients with significantly poorer five-year survival across all cohorts (all P<0.001). Transcriptomic analysis reveals that high-risk patients exhibit significant immune cell infiltration, increased expression of immune checkpoint molecules, and activation of immune-related pathways, including interferon signaling and the IL-6/JAK/STAT3 pathway. The integrated multimodal RSA model effectively predicts recurrence risk and prognosis in LAGC, enabling precise patient stratification and individualized postoperative management.