Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

Acad Radiol. 2016 Jan;23(1):62-9. doi: 10.1016/j.acra.2015.09.007. Epub 2015 Oct 26.

Abstract

Rationale and objectives: 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.

Materials and 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.

Keywords: BI-RADS; Genetic variants; Mammography; Predictive value; Risk estimation.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biopsy / methods
  • Breast / pathology*
  • Breast Neoplasms / diagnostic imaging*
  • Breast Neoplasms / genetics
  • Breast Neoplasms / pathology
  • Epidemiologic Methods
  • Female
  • Genes, BRCA1
  • Genes, BRCA2
  • Humans
  • Mammography / methods
  • Middle Aged
  • Polymorphism, Single Nucleotide / genetics
  • United States
  • Young Adult