Mammograms contain imaging biomarkers that can predict future breast cancer risk using deep learning (DL) models. We evaluated whether adding a polygenic risk score (PRS) improves performance of the ...
A risk model that combines a mammographic artificial intelligence (AI) risk score with polygenic and clinical risk scores ...
Despite major advances in genetic testing for breast cancer risk prediction, death rates remain disproportionately high among women of African ancestry. This is often due to a combination of factors, ...
Breast cancer is the most frequently diagnosed cancer among women and persists as a societal problem worldwide. It remains a leading cause of cancer associated morbidity and mortality, specifically in ...
Breast cancer is one of the most common malignancies worldwide, and mutations in the PI3K/AKT/mTOR (PAM) signaling pathway are prevalent in its development. Among these, PIK3CA mutations play a ...
Study: AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework. Image Credit: ProStockStudio / Shutterstock In a recent review published in the journal npj ...
Federal density notification and state coverage expansions rely on BI-RADS density that labels 40–50% of women, limiting precision as a trigger for supplemental screening. Mirai derived 5-year ...
Prognostic Significance of Isolated Tumor Cells and the Role of Immunohistochemistry in Nodal Evaluation in Breast Cancer: A SEER-Based Analysis and Reappraisal We used Monte Carlo simulation methods ...
Accurate detection of PIK3CA mutations is essential for personalizing breast cancer treatment, particularly with PI3K-targeted therapies. However, conventional molecular testing is not always ...
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