A study by Kaiser Permanente researchers has shown that integrating mammographic AI with polygenic risk scores and clinical risk models can improve breast cancer risk stratification, guiding both personalized breast cancer screening and chemoprevention.
The researchers calculated each woman's breast cancer risk using three different approaches:
- The Mirai mammography AI risk score, which looks for risk-related imaging biomarkers
- The Breast Cancer Surveillance Consortium version 3 clinical risk score, which considers factors such as age, race or ethnicity, family history of breast cancer, breast density, and body mass index
- The 313-SNP polygenic risk score (PRS) that assesses risk based on the presence or absence of 313 breast cancer-associated single nucleotide polymorphisms.
The C-index for the combined model was significantly higher than that for individual models with only the clinical risk score (0.62), which is used by most clinical practices, the PRS (0.61), or the Mirai score (0.66).
Importantly, the improvements in risk prediction were consistent over time and across four common self-reported racial/ethnic subgroups of Asian, Black, Latina, and White women.
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