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.
Key Words
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Article info
Publication history
Published online: October 27, 2015
Accepted:
September 28,
2015
Received in revised form:
September 15,
2015
Received:
May 15,
2015
Identification
Copyright
© 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
