Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction

Area of Science:

• Computational pathology
• Bioinformatics
• Machine learning for healthcare

Background:

• Current survival analysis models lack interpretability, limiting clinical utility.
• Existing prototype learning methods in pathology neglect tumor context and genomic data integration.
• Need for interpretable and accurate multimodal approaches in cancer prognosis.

Purpose of the Study:

• To develop an innovative prototype-based multimodal framework, FeatProto, for enhanced cancer survival prediction.
• To address limitations of traditional prototype learning by integrating global and local image features with genomic data.
• To provide traceable and interpretable decision-making in cancer prognosis.

Main Methods:

• Developed FeatProto, a unified feature prototype space integrating whole slide image (WSI) features and genomic profiles.
• Introduced a robust phenotype representation merging critical patches with global context, harmonized with genomic data.
• Implemented an Exponential Prototype Update Strategy (EMA ProtoUp) for stable cross-modal associations and adaptive prototype learning.
• Utilized a hierarchical prototype matching scheme for global centrality, local typicality, and cohort trend inference.

Main Results:

• FeatProto demonstrated superior accuracy and interpretability compared to leading unimodal and multimodal survival prediction methods.
• The framework successfully integrated diverse data modalities, enhancing predictive performance.
• Evaluations on four public cancer datasets validated the method's effectiveness.

Conclusions:

• FeatProto offers a significant advancement in prototype learning for cancer survival analysis.
• The multimodal approach provides a new perspective for interpretable and accurate clinical decision support.
• This framework holds promise for critical medical applications requiring robust prognostic tools.