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

Elizabeth S. Burnside, Jie Liu, Yirong Wu, Adedayo A. Onitilo, Catherine A. McCarty, C. David Page, Peggy L. Peissig, Amy Trentham-Dietz, Terrie Kitchner, Jun Fan, Ming Yuan

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)62-69
Number of pages8
JournalAcademic radiology
Volume23
Issue number1
DOIs
StatePublished - Jan 1 2016

Bibliographical note

Funding Information:
The authors acknowledge the support of the Wisconsin Genomics Initiative from the state of Wisconsin and support from the National Institutes of Health (grants: R01CA127379 , R01CA127379-03S1 , R01GM097618 , R01LM011028 , R01ES017400 , U54AI117924 , K24CA194251 ). We also acknowledge support from the eMERGE Network ( U01HG004608 ), the UW Institute for Clinical and Translational Research ( UL1TR000427 ), and the UW Carbone Comprehensive Cancer Center ( P30CA014520 ). The sponsors/funders of this study had no role in (1) study design; (2) collection, analysis, or interpretation of data, (3) writing of the report; or (4) decision to submit the article for publication.

Keywords

  • BI-RADS
  • Genetic variants
  • Mammography
  • Predictive value
  • Risk estimation

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