Prototyping Progressive Survival-Risk into Cross-Modal Representation Learning for Multimodal Cancer Survival Prediction

Multimodal cancer survival prediction integrates complementary information from pathological Whole Slide Images and other modalities, such as gene expression data, to estimate patient-specific survival risk. Existing methods mainly focus on individual-level cross-modal alignment, overlooking the shared risk structures among patients and the progressive nature of survival risk. To address these limitations, we propose Risk-Pro, a Progressive Survival-Risk Prototyping framework that introduces population-level risk structures into multimodal representation learning. Specifically, Risk-Pro constructs risk prototypes to capture shared prognostic patterns across patients with similar survival states and aligns patient representations with corresponding risk structures. Moreover, a Progressive Prototype Constraint is proposed to organize risk prototypes along a continuous survival trajectory, encouraging closer representations for adjacent risk states and greater separation for distant ones. Experiments on six TCGA cancer datasets demonstrate that Risk-Pro consistently outperforms state-of-the-art methods and effectively captures progressive survival-risk structures, as validated by risk trend evaluation, Kaplan-Meier survival analysis, and attribution analysis.