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

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 histopathological images. By successfully integrating this multi-tiered patient information, CATfusion has become an advanced survival prediction model to utilize the most diverse data types across various cancer types. CATfusion's architecture, which includes a bidirectional multimodal attention mechanism and self-attention block, is adept at synchronizing the learning and integration of representations from various modalities. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. The model's high accuracy in stratifying patients into distinct risk groups is a boon for personalized medicine, enabling tailored treatment plans. Moreover, CATfusion's interpretability, enabled by attention-based visualization, offers insights into the biological underpinnings of cancer prognosis, underscoring its potential as a transformative tool in oncology.