Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.

ABSTRACT: Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit significant inter-observer variability and limited predictive power. To overcome these limitations, we developed cross-attention transformer-based multimodal fusion network (CATfusion), a deep learning framework that integrates multimodal histology-genomic data for comprehensive cancer survival prediction. By employing self-supervised learning strategy with TabAE for feature extraction and utilizing cross-attention mechanisms to fuse diverse data types, including mRNA-seq, miRNA-seq, copy number variation, DNA methylation variation, mutation data, and

SUBMITTER: Hu Y 

PROVIDER: S-EPMC11926983 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature