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Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population

  • Epidemiology
  • Published:
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Summary

Background. Estimating an individual woman’s absolute risk for breast cancer is essential for decision making about screening and preventive recommendations. Although the current standard, the Gail model, is well calibrated in populations, it performs poorly for individuals. Mammographic breast density (BD) may improve the predictive accuracy of the Gail model.

Methods. Prospective observational cohort of 81,777 women in the San Francisco Mammography Registry presenting for mammography during 1993 through 2002 who had no prior diagnosis of breast cancer. Breast density was rated by clinical radiologists using the Breast Imaging Reporting and Data System classification (almost entirely fat; scattered fibroglandular densities; heterogeneously dense; extremely dense). Breast cancer cases were identified through linkage to Northern California Surveillance Epidemiology End Results (SEER) program. We compared the predictive accuracy of models with Gail risk, breast density, and the combination. All models were adjusted for age and ethnicity.

Results. During 5.1 years of follow-up, 955 women were diagnosed with invasive breast cancer. The Gail model had modest predictive accuracy (concordance index (c-index) 0.67; 95% CI 0.65–0.68). Adding breast density to the model increased the predictive accuracy to 0.68 (95% CI .66–.70, p < 0.01 compared with the Gail model alone). The model containing only breast density adjusted for age and ethnicity had predictive accuracy equivalent to the Gail model (c-index 0.67, 95% CI 0.65–0.68).

Conclusion. The addition of breast density measured by BI-RADS categories minimally improved the predictive accuracy of the Gail model. A model based on breast density alone adjusted for age and ethnicity was as accurate as the Gail model.

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Authors and Affiliations

  1. Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, USA

    Jeffrey A. Tice & Elad Ziv

  2. Department of Veteran Affairs and Department of Epidemiology & Biostatistics, General Internal Medicine Section, San Francisco, CA, USA

    Karla Kerlikowske

  3. University of California, San Francisco, CA, USA

    Steven R. Cummings

  4. San Francisco Coordinating Center, San Francisco, CA, USA

    Steven R. Cummings

Authors
  1. Jeffrey A. Tice
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  2. Steven R. Cummings
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  3. Elad Ziv
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  4. Karla Kerlikowske
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Correspondence to Jeffrey A. Tice.

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Tice, J.A., Cummings, S.R., Ziv, E. et al. Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population. Breast Cancer Res Treat 94, 115–122 (2005). https://doi.org/10.1007/s10549-005-5152-4

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