Uncertainty-Guided Iterative Contrastive Fusion for Reliable Survival Prediction in Rectal Cancer

Integrating multimodal radiological images and clinical data is critical for survival prediction in rectal cancer. However, existing methods often lack sufficient consideration of 1) modality heterogeneity (caused by rectal peristalsis, noise artifacts, and missing modalities) and 2) site heterogeneity (caused by different imaging protocols and patient populations). These factors hinder the model from capturing reliable cross-modal relationships and adapting to distribution shifts across clinical sites. In this work, we propose UICSurv, a novel multimodal Survival prediction framework highlighted by Uncertainty-guided Iterative Contrastive fusion, to capture robust cross-site multimodal interactions while leveraging sample-level uncertainty to enhance fusion reliability. Specifically, UICSurv initializes a shared multimodal embedding and iteratively refines it by fusing each heterogeneous modality via the cross-attention mechanism. In each iteration, a novel Survival Contrastive Learning (SCL) strategy is designed to progressively enhance both cross-site alignment and survival discriminability of the multimodal embedding space. Moreover, we design an EvidenceHit module, which employs temporally consistent evidential learning to jointly estimate survival probabilities and uncertainty. The estimated uncertainty further guides the embedding alignment by reducing the interference of unreliable samples. All components operate synergistically within UICSurv to reinforce reliable survival prediction in rectal cancer. Extensive experiments on multimodal datasets of rectal cancer (collected from three sites) demonstrate the superiority of our method both in survival prediction and uncertainty estimation. The code is available open-source: https://github.com/ScorpioBao/UICSurv.