Ma2SP: Missing-Aware Prompting With Modality-Adaptive Integration for Incomplete Multi-Modal Survival Prediction

Survival prediction is crucial for head and neck (H&N) cancer patients. Recently, deep learning-based multi-modal models have achieved promising performance in accurate survival prediction. However, their clinical application is hindered by the difficulty of acquiring complete sets of multi-modal data. To tackle this limitation, in this paper, we propose a novel framework, namely Ma²SP, for incomplete multi-modal survival prediction in H&N cancer. Specifically, we develop missing-aware integration (MAI) modules to align heterogeneous multi-modal data and encourage dynamic interactions among available modalities, thereby achieving flexible multi-modal integration and enhancing robustness to incomplete data. Moreover, we employ missing-aware prompting (MAP) to provide explicit guidance on missing states during training, enabling effective training with incomplete data. In addition, we introduce tumor segmentation as an auxiliary task to capture tumor-related information, which further improves prediction accuracy. Experiments demonstrate our superior performance.