title: Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density.
abstract: BACKGROUND
  To improve the discriminatory power of the Gail model for predicting absolute risk of invasive breast cancer, we previously developed a relative risk model that incorporated mammographic density (DENSITY) from data on white women in the Breast Cancer Detection Demonstration Project (BCDDP). That model also included the variables age at birth of first live child (AGEFLB), number of affected mother or sisters (NUMREL), number of previous benign breast biopsy examinations (NBIOPS), and weight (WEIGHT). In this study, we developed the corresponding model for absolute risk.
  
  
  METHODS
  We combined the relative risk model with data on the distribution of the variables AGEFLB, NUMREL, NBIOPS, and WEIGHT from the 2000 National Health Interview Survey, with data on the conditional distribution of DENSITY given other risk factors in BCDDP, with breast cancer incidence rates from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute, and with national mortality rates. Confidence intervals (CIs) accounted for variability of estimates of relative risks and of risk factor distributions. We compared the absolute 5-year risk projections from the new model with those from the Gail model on 1744 white women.
  
  
  RESULTS
  Attributable risks of breast cancer associated with DENSITY, AGEFLB, NUMREL, NBIOPS, and WEIGHT were 0.779 (95% CI = 0.733 to 0.819) and 0.747 (95% CI = 0.702 to 0.788) for women younger than 50 years and 50 years or older, respectively. The model predicted higher risks than the Gail model for women with a high percentage of dense breast area. However, the average risk projections from the new model in various age groups were similar to those from the Gail model, suggesting that the new model is well calibrated.
  
  
  CONCLUSIONS
  This new model for absolute invasive breast cancer risk in white women promises modest improvements in discriminatory power compared with the Gail model but needs to be validated with independent data.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
authors.forename: Jinbo
authors.initials: J
authors.lastname: Chen
authors.forename: David
authors.initials: D
authors.lastname: Pee
authors.forename: Rajeev
authors.initials: R
authors.lastname: Ayyagari
authors.forename: Barry
authors.initials: B
authors.lastname: Graubard
authors.forename: Catherine
authors.initials: C
authors.lastname: Schairer
authors.forename: Celia
authors.initials: C
authors.lastname: Byrne
authors.forename: Jacques
authors.initials: J
authors.lastname: Benichou
authors.forename: Mitchell H
authors.initials: MH
authors.lastname: Gail
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000473:pathology*; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging / Q000453:epidemiology* / Q000473:pathology*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D008432:Mathematical Computing; D008875:Middle Aged; D015233:Models, Statistical; D016017:Odds Ratio; D018570:Risk Assessment; D012307:Risk Factors