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

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.