• Journal of women's health · Apr 2022

    Variation in Breast Cancer Risk Model Estimates Among Women in Their 40s Seen in Primary Care.

    • Mara A Schonberg, Maria Karamourtopoulos, Adlin Pinheiro, Roger B Davis, Scot B Sternberg, Tejas S Mehta, Elizabeth A Gilliam, and Nadine M Tung.
    • Division of General Medicine, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
    • J Womens Health (Larchmt). 2022 Apr 1; 31 (4): 495502495-502.

    AbstractBackground: The Gail, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick breast cancer risk prediction models are recommended for use in primary care. Calculating breast cancer risk is particularly important for women in their 40s when deciding on mammography, with some guidelines recommending screening for those with 5-year risk similar to women age 50 (≥1.1%). Yet, little is known about risk estimate agreement among models for these women. Materials and Methods: Four hundred nine Boston-area women 40-49 years of age completed a risk questionnaire before a primary care visit to compute their breast cancer risk. The kappa statistic was used to examine when (1) Gail and BCSC agreed on 5-year risk ≥1.1%; (2) Gail estimated 5-year risk ≥1.7% and Tyrer-Cuzick estimated 10-year risk ≥5% (guideline thresholds for recommending prevention medications); and when (3) Gail and Tyrer-Cuzick agreed on lifetime risk ≥20% (threshold for breast MRI using Tyrer-Cuzick). Results: Participant mean age was 44.1 years, 56.7% were non-Hispanic white, and 7.8% had a first-degree relative with breast cancer. Of 266 with breast density information to estimate both Gail and BCSC, the models agreed on 5-year risk being ≥1.1% for 36 women, kappa = 0.34 (95% confidence interval: 0.23-0.45). Gail and Tyrer-Cuzick estimates led to agreement about prevention medications for 8 women, kappa 0.41 (0.20-0.61), and models agreed on lifetime risk ≥20% for 3 women, kappa 0.08 (-0.01 to 0.16). Conclusions: There is weak agreement on breast cancer risk estimates generated by risk models recommended for primary care. Using different models may lead to different clinical recommendations for women in their 40s.

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