DualRE-Surv: Dual-Path Complementary Fusion of Representation and Evidence for Multimodal Cancer Survival Prediction

Accurate survival prediction is vital for clinical decision. Fusing whole slide images (WSIs) and genomics data has become a key research direction, as they respectively capture the histomorphological and molecular characteristics of tumors. Existing methods typically employ feature concatenation, attention mechanisms, or cross-modal interactive learning for joint representation; however, they rarely explicitly preserve the prognostic evidence provided by different modalities at the decision level. When pathology and genomics yield inconsistent judgments regarding a patient's survival risk, modal conflicts may be propagated to the final prediction via the joint representation. To alleviate this issue, we propose DualRE-Surv, a dual-path complementary fusion of representation and evidence for multimodal cancer survival prediction. In addition to the representation path for learning pathology-genomics joint representations, we introduce a parallel evidence-aware path. This path employs Dirichlet Vacuity Estimation (DVE) to measure the sufficiency of modal evidence, utilizes Consensus-Complementarity Evidence Decomposition (CCED) to decouple cross-modal consensus evidence and complementary evidence, and enhances the discriminability of consensus evidence across different time intervals through Consensus Evidence Enhancement (CEE). Subsequently, Joint Evidential Survival (JES) integrates the two types of evidence and constructs the survival curve of the evidence path. The Representation-Evidence Survival Mixture Module further integrates the prediction results of the two paths, enabling the model to simultaneously utilize the cross-modal joint representation and the conflict-adjusted modal decision evidence. We evaluate the proposed method on eight TCGA cancer cohorts using five-fold cross-validation. DualRE-Surv achieves C-indices of 0.6477, 0.7192, 0.7318, 0.8114, 0.8120, 0.6104, 0.6830, and 0.6570 on HNSC, BLCA, BRCA, UCEC, KIRC, LUSC, SKCM, and STAD, respectively, outperforming existing methods. Furthermore, extensive validation experiments further verify the effectiveness of the overall DualRE-Surv framework and its key modules.