MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention

2026-09-18

AI Summary

为解决多模态生存预测中的数据集偏移和计算复杂性问题，MIST通过基因组引导的组织学注意力机制结合图像与基因组信息，提高了预测准确性。

Abstract

Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these challenges, we propose MIST, multimodal survival prediction with genomic-guided histology attention. MIST represents genomic features as tokens and allows them to query compact foundation-model-derived histology context tokens before survival prediction. This design enriches molecular information with histology context rather than merging separately encoded modalities only at the final stage. Training combines discrete-time survival prediction with genomic feature masking, WSI dropout, and paired WSI-genomics contrastive alignment. Across four external evaluations in colon, renal, lung, and glioblastoma cohorts, MIST improves external C-index over standard fusion baselines in the primary comparisons. These results support genomic-guided histology attention as a compact and effective strategy for multimodal oncology outcome prediction. Our code is available at https://github.com/samiyavuuz/MIST .

Problem

• multimodal survival prediction
• external cohort shift
• computational complexity

Innovation

• genomic-guided histology attention
• multimodal survival prediction
• WSI dropout
• contrastive alignment