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Medline ® Abstract for Reference 64

of 'Genetic counseling and testing for hereditary breast and ovarian cancer'

Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.
Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K
Ann Intern Med. 2008;148(5):337.
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.
Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California 94143-1732, USA. jtice@medicine.ucsf.edu