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Medline ® Abstract for Reference 64

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

64
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Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.
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Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K
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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.
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Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California 94143-1732, USA. jtice@medicine.ucsf.edu
PMID