MSSF-Surv: Multimodal State-Space Fusion for Cancer Survival Prediction

Accurate cancer survival prediction informs patient risk stratification and individualized treatment planning. Histopathology and multi-omics provide complementary prognostic evidence, but their integration can be limited by the loss of tissue context during patch aggregation and insufficient modeling of cross-modal interactions. We propose Multimodal State Space Fusion (MSSF-Surv), a framework that preserves spatial organization before conditioning pathology readout on molecular profiles. Its dual-granularity design captures tissue architecture alongside cellular morphology, while multidirectional state-space modeling handles long sequences of fine-grained whole-slide image patches with linear complexity. Molecular queries then retrieve information from these spatially contextualized representations, rather than from isolated patch features. A shared state-space model combines the retrieved features with omics representations through modality-specific gates, linking tissue context to molecular information for survival prediction. Extensive experiments using five-fold cross-validation on six TCGA cancer cohorts demonstrate the effectiveness of MSSF-Surv. Survival analyses and ablations support its predictive performance and the contributions of dual-granularity encoding and omics-guided fusion.