Breast Cancer Risk

Accurately assessing breast cancer risk is crucial for effective screening and prevention, driving research into improved risk prediction models. Current efforts focus on leveraging longitudinal mammogram data, incorporating multiple image views and radiomic features, and employing machine learning techniques like deep learning (including transformer networks) and ensemble methods to improve prediction accuracy and interpretability. These advancements aim to personalize risk assessment, potentially leading to more targeted screening strategies, reduced unnecessary biopsies, and improved patient outcomes. The ultimate goal is to optimize breast cancer detection and management through more precise and efficient risk stratification.