AI and polygenic scores improve breast cancer risk assessment

Pleasanton, CA (June 23, 2026) — A risk model that combines a mammographic artificial intelligence (AI) risk score with polygenic and clinical risk scores more accurately identifies women at high risk of developing breast cancer than clinical risk scores used alone, a new Kaiser Permanente study found.

The study, published in the Journal of the National Cancer Institute, is one of the largest and most diverse to evaluate the ability of 3 approaches — mammography AI, polygenic, and clinical — to predict breast cancer risk.

“Breast cancer risk tools can help identify high-risk women who are most likely to benefit from more frequent breast cancer screening or medications to help reduce risk,” said lead author Vignesh Arasu, MD, PhD, a radiologist and research scientist at the Kaiser Permanente Division of Research. “Our study shows that each of the approaches  identifies a distinct group of women, and that when all 3 risk tests are used we increase our ability to differentiate high-risk and low-risk women and provide more personalized screening recommendations.”

The study included 82,957 women enrolled between 2003 and 2020 in the Kaiser Permanente Research Bank, a national program that currently includes medical records, survey, and genetic data from more than 400,000 Kaiser Permanente members. All the women included in the study had a recent mammogram that showed no signs of breast cancer. None had a genetic mutation known to increase breast cancer risk or had previously been diagnosed with breast cancer. 

Over 10 years, 2,471, or 3%, of the women in the study were diagnosed with invasive breast cancer or ductal carcinoma in situ (DCIS). The risk model that incorporated all 3 risk assessment methods was the most accurate, with a C-index score of .70. On its own, the clinical risk tool scored .62, while the polygenic test’s prediction accuracy was .61. Combining the clinical and the polygenic risk scores resulted in a prediction accuracy of .66.

The study also found that among the women at highest risk of developing breast cancer, the clinical risk score alone identified 19% of the women who went on to develop breast cancer over a 10-year period while the combined model identified 26% of these women.

“From a modeling perspective, the key result is the consistent improvement in prediction accuracy when these risk scores are combined,” said study co-author Stacey E. Alexeeff, PhD, a research scientist and biostatistician at DOR. “We looked across different time horizons within the 10-year follow-up, and the combined model consistently performed better than each risk score alone.”

Clinical risk models have been used since 1989 to assess breast cancer risk; polygenic risk scores were introduced about 25 years later. Mammographic AI algorithms have been studied since 2022.

The mammography AI algorithm predicts 5-year breast cancer risk based on the presence of risk-related imaging biomarkers it can detect on a mammogram. The clinical risk score considers factors such as age, race or ethnicity, family history of breast cancer, breast density, and body mass index. The polygenic risk score is determined by the presence or absence of 313 single nucleotide polymorphisms (SNPs) that prior studies have found to be associated with breast cancer. 

The new study builds on a prior study led by Dr. Arasu that found AI mammography risk assessment was better at predicting future breast cancer risk than a clinical risk model.

“As technologies for assessing risk continue to improve,” Dr. Arasu added, “we are likely to see even more improvement in our ability to predict risk.”

Vignesh A Arasu, Tejomay Gadgil, Joseph H Rothstein, Stacey E Alexeeff, Ninah S Achacoso, Arjun Bhattacharya, Jason B Cord, Laura J Esserman, Woodward Galbraith, Lawrence D Gerstley, Susan Taylor Head, Nola M Hylton, Lawrence H Kushi, Catherine Lee, Amethyst D Leimpeter, Donald A Lewis, Vincent Liu, Ben J Marafino, Laurie R Margolies, Daniel A Navarro, Albert Pu, Lori C Sakoda, Jun Shan, Yiwey Shieh, Adriana Sistig, Cara L Smith Gueye, Laura van’t Veer, Marvella Villaseñor, Mark Westley, Dorota J Wisner, Jeffrey A Tice, Li Shen, Laurel A Habel, Weiva Sieh, Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer risk models in the Kaiser Permanente Research Bank, JNCI: Journal of the National Cancer Institute, 2026;, djag154, https://doi.org/10.1093/jnci/djag154