Breast cancer is a highly complex and heterogeneous malignancy driven by an intricate interplay of environmental exposures and genetic factors, most notably mutations in the BRCA1 and BRCA2 genes. Assessing these genetic risks has traditionally relied on isolated statistical methods or manual pedigree analyses, which often struggle to capture the complex, high-dimensional nature of genomic and phenotypic data. This paper explores the landscape of computational approaches, including deep neural networks and multimodal fusion techniques, to predict breast cancer survival and comprehensively identify genetic risk profiles. By integrating advanced machine learning models, we propose a holistic methodology that fuses genetic variant sequences with clinical records and histopathological imaging data to stratify patient risk more accurately. The findings suggest that employing deep learning for polygenic risk scoring and multimodal feature fusion significantly enhances predictive accuracy, ultimately paving the way for highly personalized therapeutic interventions and improved clinical outcomes.
Khandelwal et al. (Thu,) studied this question.