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. 2008 Mar 4;148(5):337-47.
doi: 10.7326/0003-4819-148-5-200803040-00004.

Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model

Jeffrey A Tice  1 , Steven R Cummings, Rebecca Smith-Bindman, Laura Ichikawa, William E Barlow, Karla Kerlikowske
Affiliations

Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model

Jeffrey A Tice et al. Ann Intern Med. .

Abstract

Background: Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography.

Objective: To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density.

Design: Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort.

Setting: Screening mammography sites participating in the Breast Cancer Surveillance Consortium.

Patients: 1,095,484 women undergoing mammography who had no previous diagnosis of breast cancer.

Measurements: Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories.

Results: During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women. The breast density model was well calibrated overall (expected-observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years.

Limitation: The model has only modest ability to discriminate between women who will develop breast cancer and those who will not.

Conclusion: A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.

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Conflict of interest statement

Potential Financial Conflicts of Interest: Consultancies: S.R. Cummings (Eli Lilly). Honoraria: S.R. Cummings (Eli Lilly). Grants received: J.A. Tice (Building Interdisciplinary Careers in Women's Health [career development award]), S.R. Cummings (Eli Lilly, Lilly Foundation). Grants pending: S.R. Cummings (Eli Lilly, Lilly Foundation).

Figures

Appendix Figure
Appendix Figure. The Breast Cancer Surveillance Consortium breast density model algorithm
BI-RADS = Breast Imaging Reporting and Data System; SEER = Surveillance, Epidemiology, and End Results.
See this image and copyright information in PMC

Summary for patients in

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