PathRoute: Active Evidence Routing for Cancer Survival Prediction on Whole Slide Pathology Images

Whole slide images (WSIs) are central to cancer diagnosis and prognosis. However, mainstream survival prediction methods directly map massive tile features into a single global representation for risk readout, failing to explicitly distinguish complex histological composition, ignoring cancer-specific clinical phenotype differences, and leaving the model’s internal reasoning process opaque. To address these issues, we propose PathRoute, an active evidence routing framework for interpretable WSI survival prediction. The framework models WSI reasoning as a two-stage directional process that first forms evidence and then reads out risk: four role-specific learnable queries collaborate in a shared latent space, respectively performing tissue-composition sensing, active evidence routing, phenotype-semantic auxiliary supervision, and risk readout; a directional attention mask further constrains forward visibility between queries, so that risk prediction is structurally realized as a downstream readout of pre-formed evidence representations, rather than an end-to-end black-box mapping. On five TCGA solid tumor cohorts under unified inputs and identical patient-level 5-fold cross-validation splits, PathRoute using WSI alone achieves higher average C-index than representative multimodal and WSI-only methods, validating the effectiveness of structured evidence organization. Further analysis shows that routing queries consistently and spontaneously concentrate attention onto tumor regions across all five cancer types without any tissue-type supervision, providing direct interpretability support for the active routing mechanism. Code available at https://github.com/ZephyrGo/pathRoute.