Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction

TL;DR

DIMAFx is an explainable multimodal framework that improves cancer survival prediction by producing disentangled, interpretable representations from histopathology images and transcriptomics data, revealing key biological insights.

Contribution

We introduce DIMAFx, a novel framework that achieves state-of-the-art performance while providing interpretability through disentangled multimodal representations and SHAP explanations.

Findings

DIMAFx outperforms existing models across multiple cancer cohorts.

It reveals biologically meaningful features, such as tumor morphology and pathway activity.

Interpretable features align with known cancer biology.

Abstract

While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how different data sources influence predictions. To address this, we introduce DIMAFx, an explainable multimodal framework for cancer survival prediction that produces disentangled, interpretable modality-specific and modality-shared representations from histopathology whole-slide images and transcriptomics data. Across multiple cancer cohorts, DIMAFx achieves state-of-the-art performance and improved representation disentanglement. Leveraging its interpretable design and SHapley Additive exPlanations, DIMAFx systematically reveals key multimodal interactions and the biological information encoded in the disentangled representations. In breast cancer survival prediction, the most predictive features contain modality-shared information, including one capturing solid tumor morphology contextualized primarily by late estrogen response, where higher-grade morphology aligned with pathway upregulation and increased risk, consistent with known breast cancer biology. Key modality-specific features capture microenvironmental signals from interacting adipose and stromal morphologies. These results show that multimodal models can overcome the traditional trade-off between performance and explainability, supporting their application in precision medicine.