Early and hereditary breast cancer: advances in risk stratification and imaging approaches

Abstract

Breast cancer (BC) remains a leading global health challenge, characterized by significant heterogeneity that complicates its detection, diagnosis, and management. The integration of imaging biomarkers and radiomics into clinical workflows has revolutionized early detection, risk stratification, and personalized treatment strategies. Established modalities, such as mammography and magnetic resonance imaging, in conjunction with biomarkers like hormone receptor status, continue to play a pivotal role in guiding therapeutic decisions. Simultaneously, advancements in radiomics and artificial intelligence (AI) have enabled the extraction and analysis of high-dimensional imaging data, offering novel insights into tumor biology and predicting treatment outcomes. This review explores the synergy of imaging biomarkers, radiomics, and AI, emphasizing their potential to transform BC care through enhanced precision and optimized patient outcomes.