title: One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.
abstract: BACKGROUND
  One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure.
  
  
  METHODS
  We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death.
  
  
  RESULTS
  The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model.
  
  
  CONCLUSIONS
  The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density.
  
  
  CONCLUSIONS
  A two-density model should be considered for women whose density decreases when calculating breast cancer risk.
authors.affiliation: Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California. General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, San Francisco, California. Karla.Kerlikowske@ucsf.edu.
authors.forename: Karla
authors.initials: K
authors.lastname: Kerlikowske
authors.affiliation: Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, New Mexico.
authors.forename: Charlotte C
authors.initials: CC
authors.lastname: Gard
authors.affiliation: Department of Surgery and Vermont Cancer Center, University of Vermont, Burlington, Vermont.
authors.forename: Brian L
authors.initials: BL
authors.lastname: Sprague
authors.affiliation: Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California.
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Tice
authors.affiliation: Department of Public Health Sciences, University of California, Davis, Davis, California. Group Health Research Institute, Group Health Cooperative, Seattle, Washington.
authors.forename: Diana L
authors.initials: DL
authors.lastname: Miglioretti
mesh_terms: D000328:Adult; D000368:Aged; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000208:ethnology; D055088:Early Detection of Cancer; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008327:Mammography; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D018426:SEER Program; D014481:United States / Q000453:epidemiology
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