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

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

Author information

1
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.
2
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin.
3
Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI 53792-3252.
4
Marshfield Clinic Research Foundation, Marshfield, Wisconsin; Department of Hematology/Oncology, Marshfield Clinic Weston Center, Weston, Wisconsin.
5
Essentia Institute of Rural Health, Duluth, Minnesota.
6
Marshfield Clinic Research Foundation, Marshfield, Wisconsin.
7
Department of Population Health Sciences, University of Wisconsin, Madison, Wisconsin.
8
Department of Statistics, University of Wisconsin, Madison, Wisconsin; Morgridge Institute for Research, Madison, Wisconsin.

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

PMID:
26514439
PMCID:
PMC4684977
DOI:
10.1016/j.acra.2015.09.007
[Indexed for MEDLINE]
Free PMC Article

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