Multimodal fusion strategies for survival prediction in breast cancer: A comparative deep learning study.

ABSTRACT: Accurate survival prediction in breast cancer remains a key challenge in oncology, requiring models that can integrate diverse clinical, molecular, and imaging data sources to guide breast cancer management. While recent deep learning models have explored multimodal integration for cancer survival prediction, their generalizability to unseen data remains limited. In this study, we developed and optimized unimodal and multimodal models for breast cancer survival prediction, systematically assessing our optimized early and late integration strategies and their impact on out-of-sample generalization performance. We integrated clinical variables, somatic mutations, RNA expression, copy number variation, miRNA expression, and histopathology images from The Cancer Genome Atlas breast cancer data

SUBMITTER: Sucre A 

PROVIDER: S-EPMC12595345 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature