title: Predicting invasive breast cancer versus DCIS in different age groups.
abstract: BACKGROUND
  Increasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.
  
  
  METHODS
  We analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).
  
  
  RESULTS
  The models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.
  
  
  CONCLUSIONS
  Clinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age.
authors.forename: Mehmet U S
authors.initials: MU
authors.lastname: Ayvaci
authors.forename: Oguzhan
authors.initials: O
authors.lastname: Alagoz
authors.forename: Jagpreet
authors.initials: J
authors.lastname: Chhatwal
authors.forename: Alejandro
authors.initials: A
authors.lastname: Munoz del Rio
authors.forename: Edward A
authors.initials: EA
authors.lastname: Sickles
authors.forename: Houssam
authors.initials: H
authors.lastname: Nassif
authors.forename: Karla
authors.initials: K
authors.lastname: Kerlikowske
authors.affiliation: Industrial & Systems Engineering, University of Wisconsin, 1513 University Avenue, Madison, WI 53706, USA. EBurnside@uwhealth.org.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D001943:Breast Neoplasms / Q000453:epidemiology / Q000473:pathology*; D018270:Carcinoma, Ductal, Breast / Q000453:epidemiology / Q000473:pathology*; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000453:epidemiology / Q000473:pathology*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D008875:Middle Aged; D012307:Risk Factors
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