Integrating histopathology and genomic data: a comparative study of fusion methods for breast cancer survival prediction
Abstrak
Abstract Accurate breast cancer survival prediction using multi-modal data is vital for enhancing clinical decisions. This study evaluates deep learning based fusion strategies, early, intermediate, late, and a hybrid approach, to integrate histopathology images and genomic data for one year survival prediction. We developed a robust evaluation framework, employing tailored deep learning architectures and metrics including accuracy, precision, recall, F1 score, and AUC. Model performance was validated using Kaplan–Meier curves and log-rank tests, with SHAP-based feature importance analysis enhancing interpretability. Results highlight the strengths and limitations of each fusion strategy, offering insights into optimal multi-modal learning approaches for breast cancer prognosis. Our findings underscore the importance of selecting task specific fusion methods, providing a reproducible, interpretable framework to advance survival prediction. All code and configurations are publicly available.
Topik & Kata Kunci
Penulis (5)
Younes Akbari
Faseela Abdullakutty
Somaya Al Maadeed
Ahmed Bouridane
Rifat Hamoudi
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1007/s40747-025-02133-y
- Akses
- Open Access ✓