Global-to-Instance Structural Alignment for Pathway-based Multi-view Learning in Multimodal Cancer Survival Prediction

Multimodal cancer survival prediction aims to leverage complementary information from histopathological images and gene expression to estimate patient-specific prognostic risks. Recent approaches organize genomic information into biological pathways, forming a multi-view learning framework where each pathway serves as an individual view jointly modeled with the shared histopathological modality. However, these methods often treat pathways as independent sources, overlooking complex inter-view relationships among biological pathways. We investigate pathway structures from a population perspective and identify heterogeneous cross-pathway correlations and pathway-survival associations across cancer cohorts. Motivated by these observations, we propose StructAlign, a global-to-instance alignment framework that incorporates population-level pathway structures into patient-specific multi-view representation learning. StructAlign introduces cross-pathway and pathway-survival alignment objectives to preserve inter-pathway dependencies and guide pathway aggregation toward survival-associated patterns. Extensive experiments across multiple cancer cohorts demonstrate that StructAlign improves survival prediction and interpretability through consistent pathway structure preservation, survival-aware view selection, and biologically meaningful attribution.