Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

4 March 2008 | Volume 148 Issue 5 | Pages 337-347

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.

Editors' Notes Context Existing breast cancer prediction tools do not account for breast density, a strong risk factor for breast cancer and have been studied in white women only. Contribution The authors developed a breast cancer risk prediction model that incorporates a measure of breast density routinely reported with mammography. Its predictions were accurate, but it had only modest ability to distinguish women who did not develop cancer from those who did, and it misclassified risk in some subgroups. Implication The model requires validation in additional populations. A breast cancer prediction model that incorporates breast density does well in some but not all domains of predicting risk. Its accuracy should be better characterized before it is used clinically. —The Editors

 

Author and Article Information

From University of California, San Francisco, San Francisco, California, and Cancer Research and Biostatistics and University of Washington, Seattle, Washington.

Acknowledgment: The authors thank the BCSC investigators, participating mammography facilities, and radiologists for the data they provided for the study. A list of the BCSC investigators and procedures for requesting BCSC data for research purposes are available at http://breastscreening.cancer.gov/.

Grant Support: By the National Cancer Institute–funded Breast Cancer Surveillance Consortium cooperative agreement (grants U01CA63740, U01CA86076, U01CA86082, U01CA63736, U01CA70013, U01CA69976, U01CA63731, and U01CA70040) and a Building Interdisciplinary Research Careers in Women's Health faculty development grant (K12 AR47659).

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).

Reproducible Research Statement: The data set is available through the BCSC Web site (available at http://breastscreening.cancer.gov/).

Requests for Single Reprints: Jeffrey A. Tice, MD, Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, 1701 Divisadero Street, Suite 554, San Francisco, CA 94143-1732; e-mail, jtice{at}medicine.ucsf.edu.

Current Author Addresses: Dr. Tice: Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, 1701 Divisadero Street, Suite 554, San Francisco, CA 94143-1732.

Dr. Cummings: San Francisco Coordinating Center, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107.

Dr. Smith-Bindman: University of California, San Francisco, 185 Berry Street, Suite 350, San Francisco, CA 94143.

Ms. Ichikawa: The Center for Health Studies, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101-1448.

Dr. Barlow: Cancer Research and Biostatistics, 1730 Minor Avenue, Suite 1900, Seattle, WA 98101.

Dr. Kerlikowske: University of California, San Francisco, 4150 Clement Street, San Francisco, CA 94121.

Author Contributions: Conception and design: J.A. Tice, S.R. Cummings, W.E. Barlow, K. Kerlikowske.

Analysis and interpretation of the data: J.A. Tice, S.R. Cummings, W.E. Barlow, K. Kerlikowske.

Drafting of the article: J.A. Tice, K. Kerlikowske.

Critical revision of the article for important intellectual content: J.A. Tice, S.R. Cummings, L. Ichikawa, K. Kerlikowske.

Final approval of the article: J.A. Tice, S.R. Cummings, L. Ichikawa, W.E. Barlow, K. Kerlikowske.

Provision of study materials or patients: K. Kerlikowske.

Statistical expertise: J.A. Tice, W.E. Barlow.

Obtaining of funding: J.A. Tice, W.E. Barlow, K. Kerlikowske.

Administrative, technical, or logistic support: S.R. Cummings, K. Kerlikowske.

Collection and assembly of data: L. Ichikawa, W.E. Barlow, K. Kerlikowske.

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