ArticleComputational and structural biotechnology journal2025
Multimodal fusion strategies for survival prediction in breast cancer: A comparative deep learning study.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Hybrid deep learning time-to-event modeling of major adverse cardiovascular events using coronary artery calcium score scans.European journal of radiology artificial intelligence · 2026Article
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- PIMO: pathway-based interpretable multiomics interactions for multiomics integration.Bioinformatics (Oxford, England) · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- MRI-Based Deep Learning Guides Multi-Omics Discovery of NBPF4 as a Therapeutic Target for Breast Cancer Lymph Node Metastasis.Research (Washington, D.C.) · 2026Article
- An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.Computational and structural biotechnology journal · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate survival prediction in breast cancer remains a key challenge in oncology, requiring models that can integrate diverse clinical, molecular, and imaging data sources to guide breast cancer management. While recent deep learning models have explored multimodal integration for cancer survival prediction, their generalizability to unseen data remains limited. In this study, we developed and optimized unimodal and multimodal models for breast cancer survival prediction, systematically assessing our optimized early and late integration strategies and their impact on out-of-sample generalization performance. We integrated clinical variables, somatic mutations, RNA expression, copy number variation, miRNA expression, and histopathology images from The Cancer Genome Atlas breast cancer dataset. Across all modality combinations, late fusion models consistently outperformed early fusion approaches and late and intermediate benchmark methods, with the combination of omics and clinical data yielding the highest test-set concordance indices. Explainability analyses showed that our models captured biologically relevant features associated with patient survival. These findings highlight the value of late-fusion multimodal deep learning frameworks for robust and explainable survival prediction in breast cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.