title: Prospective breast cancer risk prediction model for women undergoing screening mammography.
abstract: BACKGROUND
  Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography.
  
  
  METHODS
  There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided.
  
  
  RESULTS
  Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women.
  
  
  CONCLUSIONS
  Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.
authors.affiliation: Cancer Research and Biostatistics, 1730 Minor Avenue, Suite 1900, Seattle, WA 98101, USA. williamb@crab.org
authors.forename: William E
authors.initials: WE
authors.lastname: Barlow
authors.forename: Emily
authors.initials: E
authors.lastname: White
authors.forename: Rachel
authors.initials: R
authors.lastname: Ballard-Barbash
authors.forename: Pamela M
authors.initials: PM
authors.lastname: Vacek
authors.forename: Linda
authors.initials: L
authors.lastname: Titus-Ernstoff
authors.forename: Patricia A
authors.initials: PA
authors.lastname: Carney
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Tice
authors.forename: Diana S M
authors.initials: DS
authors.lastname: Buist
authors.forename: Berta M
authors.initials: BM
authors.lastname: Geller
authors.forename: Robert
authors.initials: R
authors.lastname: Rosenberg
authors.forename: Bonnie C
authors.initials: BC
authors.lastname: Yankaskas
authors.forename: Karla
authors.initials: K
authors.lastname: Kerlikowske
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology* / Q000517:prevention & control; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging / Q000453:epidemiology; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008327:Mammography; D008403:Mass Screening; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D016017:Odds Ratio; D017698:Postmenopause; D011237:Predictive Value of Tests; D017697:Premenopause; D011379:Prognosis; D011446:Prospective Studies; D012042:Registries; D018570:Risk Assessment; D012307:Risk Factors; D018426:SEER Program; D014481:United States / Q000453:epidemiology