BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion

Breast cancer is a significant health concern affecting millions of women worldwide.Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes.Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients.We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features.These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features.A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them.Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration.Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge.Our model achieves a mean concordance index of 0.77 and a timedependent area under the curve of 0.84, outperforming state-of-the-art methods.It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99,95% CI: 1.88-4.78,p<0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91,95% CI: 1.80-4.68,p<0.005).