BioFusionNet: Deep learning-based survival risk stratification in ER+ breast cancer through multifeature and multimodal data fusion
Open accessAbstract
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 time-dependent 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).
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Alwakid, G. N., Tariq, N., Humayun, M., Mujtaba, H., & Rubab, K. (2026) · Journal of Disability Research · doi:10.57197/jdr-2025-0834
Wang, Z.-Y., Liu, N., & Chen, M. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1875788
Machine Learning for Survival Analysis
Bender, A., & Sonabend, R. (2026) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003414292
- Authors
- Mondol, R. K., Millar, E. K. A., Sowmya, A., & Meijering, E.
- Affiliations
- UNSW Sydney (AU)
- St George Hospital (AU)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2024-09
- Volume
- 28
- Issue
- 9
- Pages
- 5290-5302
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4391947857
- Cited by
- 29
- References
- 63
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 2
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Computational Infrastructure
- Australian Government
- University of New South Wales
- National Cancer Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- estrogen receptor-positive breast cancer, survival risk stratification, multimodal fusion, genetic data integration, clinical data integration, self-supervised feature extraction, DINO, variational autoencoder, cross-attention, overall survival prediction, prognostic model
- MeSH terms
- Deep Learning, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted, Survival Analysis, Risk Assessment
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