Stacked Multimodal Fusion of Genetic and Histopathology Features for Survival Prediction in Breast Cancer

Accurate survival prediction in breast cancer requires capturing complementary information at both the molecular and tissue morphology levels, which single-modality models fail to represent effectively. We propose a multimodal survival prediction framework that integrates genetic expression features and whole-slide histopathology representations. Genetic embeddings are learned using a multi-head attention network designed to produce stable low-dimensional representations from high-dimensional transcriptomic profiles. Tissue morphology features are extracted from segmented whole-slide images using a fine-tuned ConvNeXt-Tiny backbone. Each modality is used to train an independent Cox proportional hazards model, producing unimodal risk estimates. These modality-specific risk scores are then combined through a stacked fusion strategy, where a meta-model learns the final survival prediction. Evaluated on the TCGA-BRCA dataset, the proposed approach achieves a concordance index of 0.666, outperforming both unimodal baselines and standard multimodal fusion strategies. To demonstrate generalizability, the framework was externally validated across three additional pan-cancer cohorts (LUAD, BLCA, KIRC). The stacked fusion consistently outperformed unimodal baselines across all cohorts with a mean cross-cancer Concordance Index (C-index) of 0.684. These results demonstrate that leveraging both molecular and histological signals jointly improves the robustness and discriminative power of patient-level survival prediction.