BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+
  Breast Cancer Through Multifeature and Multimodal Data Fusion
Raktim Kumar Mondol Ewan K. A. Millar Arcot Sowmya Erik Meijering
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 achieve a holistic patient profile
and perform survival risk stratification of ER+ breast cancer patients. We
employ multiple self-supervised feature extractors, namely DINO and MoCoV3,
pretrained on histopathology patches to capture detailed histopathological
image features. We then utilise a variational autoencoder (VAE) to fuse these
features, and harness the latent space of the VAE to feed into a self-attention
network, generating patient-level features. Next, we develop a
co-dual-cross-attention mechanism to combine 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 (FFN),
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 in the field. The proposed model achieves a mean concordance index
(C-index) of 0.77 and a time-dependent area under the curve (AUC) of 0.84,
outperforming state-of-the-art methods. It predicts risk (high versus low) with
prognostic significance for overall survival (OS) 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). The proposed method not only improves
model performance but also addresses a critical gap in handling imbalanced
data.