Research Article: Enhancing multimodal survival prediction: tri-modal learning with clinical knowledge integration via state space models

Abstract:
Accurate survival prediction is crucial for precision oncology, yet it faces challenges due to the neglect of clinical priors and high computational complexity. We propose TriBind-Mamba, a tri-modal framework integrating Clinical Knowledge Prompting (CKP) and selective State Space Models (SSMs). By transforming structured clinical records into semantic narratives using Large Language Models (LLMs), our model provides high-level context for morphological and molecular features. TriBind-Mamba efficiently processes gigapixel whole slide images and transcriptomic profiles with linear complexity, achieving state-ofthe-art performance (Overall C-index of 0.664) across five TCGA cohorts while significantly reducing computational overhead. Interpretability is enhanced by integrating human-readable clinical knowledge prompts, biologically meaningful pathway-level transcriptomic tokens, and WSI attention heatmaps that project model-derived importance scores back onto histopathological regions. These analyses suggest that TriBind-Mamba focuses on prognostically relevant malignant areas, providing a more transparent basis for multimodal survival prediction.

Introduction:
Survival analysis, also known as time-to-event analysis, focuses on the probabilistic assessment of experiencing a specified event, such as mortality or recurrence, within a clinical setting before a given time under both uncensored and right-censored data ( 1 – 3 ). In the realm of cancer management, survival prediction serves as a cornerstone of precision oncology, enabling the quantification of prognostic risks and assisting clinicians in formulating more targeted diagnostic decisions, patient stratification,…

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