SAMamba: Integrating State Space Model for Enhanced Multi-modal Survival Analysis

Survival prediction represents a challenging ordinal regression task, involving modeling of intricate interactions among various data modalities. The recent evolution of state space models, Mamba in particular, has opened new vistas for effectively processing sequence data, including genomic profiles and gigapixel pathology Whole Slide Images (WSIs). In light of these advancements, we propose Survival Analysis Mamba (SAMamba), a novel approach that melds the Mamba framework with multi-modal survival prediction. Specifically, we propose a patch clustering layer to identify morphological prototypes from the extensive collection of patches within WSIs and employ gene set enrichment analysis to explore the biological associations between pathways and gene sets for enhanced and robust feature representation. Subsequently, we introduce Mamba structures to capture the intrinsic relationships within pathology WSIs and genomic profiles with linear computational complexity. Additionally, we utilize multi-modal attention to seamlessly integrate multi-modal data and design a self-attention pooling module to further refine insights from each data modality for enhanced survival outcome prediction. Extensive experiments on four public TCGA datasets are conducted to validate the effectiveness of our proposed SAMamba, using ablation studies, statistical analysis, and visualization. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art methods, highlighting the potential of the proposed SAMamba for multi-modal survival outcome prediction. Our code will be released at https://github.com/coffeeNtv/SAMamba.