Background: The 313-variant polygenic risk score (PRS) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS across different European populations which could influence risk estimation has not been performed.
Methods: We explored the distribution of PRS across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank.
Menopausal hormone therapy (MHT) users are at increased breast cancer (BC) risk and decreased colorectal cancer (CRC) risk compared with never users, but these opposing associations might differ by familial risk of BC and CRC. We harmonized data from three cohorts and generated separate BC and CRC familial risk scores (FRS) based on cancer family history. We defined moderate/strong family history as FRS ≥ 0.
View Article and Find Full Text PDFArtificial intelligence (AI) is enabling us to delve deeply into the information inherent in a mammogram and identify novel features associated with high risk of a future breast cancer diagnosis. Here, we discuss how AI is improving mammographic density-associated risk prediction and shaping the future of screening and risk-reducing strategies.
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