title: Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy.
abstract: OBJECTIVE
  The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors.
  
  
  METHODS
  Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method.
  
  
  RESULTS
  The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001).
  
  
  CONCLUSIONS
  BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.
authors.affiliation: Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI 53792-3252; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin. Electronic address: eburnside@uwhealth.org.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI 53792-3252.
authors.forename: Yirong
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, Wisconsin; Department of Hematology/Oncology, Marshfield Clinic Weston Center, Weston, Wisconsin.
authors.forename: Adedayo A
authors.initials: AA
authors.lastname: Onitilo
authors.affiliation: Essentia Institute of Rural Health, Duluth, Minnesota.
authors.forename: Catherine A
authors.initials: CA
authors.lastname: McCarty
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin.
authors.forename: C David
authors.initials: CD
authors.lastname: Page
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, Wisconsin.
authors.forename: Peggy L
authors.initials: PL
authors.lastname: Peissig
authors.affiliation: Department of Population Health Sciences, University of Wisconsin, Madison, Wisconsin.
authors.forename: Amy
authors.initials: A
authors.lastname: Trentham-Dietz
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, Wisconsin.
authors.forename: Terrie
authors.initials: T
authors.lastname: Kitchner
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin; Morgridge Institute for Research, Madison, Wisconsin.
authors.forename: Jun
authors.initials: J
authors.lastname: Fan
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin; Morgridge Institute for Research, Madison, Wisconsin.
authors.forename: Ming
authors.initials: M
authors.lastname: Yuan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001706:Biopsy / Q000379:methods; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000235:genetics / Q000473:pathology; D004812:Epidemiologic Methods; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D006801:Humans; D008327:Mammography / Q000379:methods; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D014481:United States; D055815:Young Adult
keywords: BI-RADS; Genetic variants; Mammography; Predictive value; Risk estimation