MCAMamba: A multimodal method with bidirectional cross-attention and state space model for cancer survival prediction

Cancer survival prediction is crucial for clinical decision-making and personalized treatment planning. The joint analysis of pathological images and genomic profiles provides complementary information at both histological and molecular levels, offering a more comprehensive foundation for patient prognosis assessment. However, existing multimodal survival prediction methods face two major challenges: (1) How to efficiently capture global dependencies in high-dimensional, long sequence features while maintaining linear complexity? (2) How to fully preserve and utilize the inherent valuable information of each modality while achieving cross-modal interaction? To address these challenges, we propose MCAMamba, a multimodal method with bidirectional cross-attention and a state space model for cancer survival prediction. This method employs a parallel encoder-decoder architecture, leveraging Mamba's efficient long sequence modeling capabilities to capture global dependencies and discriminative features within each modality. Meanwhile, a Bidirectional Cross-Attention module is integrated into the framework to achieve semantic alignment and complementary information exchange across modalities, enhancing the prediction of patient survival risk. Experimental results on four public TCGA cancer datasets (BLCA, BRCA, UCEC, and LUAD) demonstrate that MCAMamba significantly outperforms existing methods in predictive performance. The c-index improves by 2.47%-17.9%, validating the superior performance of the method in multimodal cancer survival prediction.