"Risk prediction models can identify individuals at increased risk for breast cancer at younger ages, allowing for risk stratification and the implementation of earlier or supplemental screening and preventive interventions for high-risk women, thereby improving breast cancer outcomes. Individuals identified as high risk for breast cancer (≥20% lifetime risk) may be advised to (1) initiate screening mammography at an earlier age than recommended for those at average risk, (2) supplement mammography with additional imaging modalities, such as breast MRI, and (3) undergo genetic counseling and testing, as appropriate. The recommendation for supplemental breast cancer screening with breast MRI in females with a lifetime risk threshold of ≥20% is based on the 2007 American Cancer Society (ACS) screening recommendation for women at high risk.¹ In this consensus-based statement, ACS outlined that the threshold for “high risk” at which breast MRI would be appropriate was “a lifetime risk of breast cancer of about 20% to 25% or greater, according to risk assessment tools that are based mainly on family history.” However, existing breast cancer risk prediction models have primarily been developed in White females and may underestimate risks in Black females.² Furthermore, recent data have suggested that 5- and 10-year risks may be more accurate than lifetime risks, particularly in females aged <40 years.³

Beyond risk factors included in risk prediction models, such as personal characteristics, hormonal and lifestyle factors, and personal breast and family cancer history, inherited breast cancer genes also increase breast cancer risk and are important to consider.⁴ Beyond inherited breast cancer genes that raise breast cancer risk, emerging data suggest that single nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS) may be used to generate polygenic risk scores (PRS), which may further refine breast cancer risk.⁵ However, genetic data have also been primarily studied in European ancestry females, which may further reduce the ability to accurately stratify breast cancer risk in African and other non-European ancestry populations.⁶

Given that the high-risk threshold was set >2 decades ago by the ACS based on a consensus statement, more contemporary considerations are likely needed to understand breast cancer risk more broadly across all populations based on updated evidence.⁷ Current data gaps highlight the importance of representation of all populations in both the development of risk prediction models and, more broadly, in the conduct of risk and genomics research. Although new analytic methods and models are being developed to improve risk stratification across all populations, it remains critical to assess the utility and calibration of existing and new models to ensure applicability across non-European ancestry populations."