Share Email Print

Proceedings Paper

Discriminatory power of common genetic variants in personalized breast cancer diagnosis
Author(s): Yirong Wu; Craig K. Abbey; Jie Liu; Irene Ong; Peggy Peissig; Adedayo A. Onitilo; Jun Fan; Ming Yuan; Elizabeth S. Burnside
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Technology advances in genome-wide association studies (GWAS) has engendered optimism that we have entered a new age of precision medicine, in which the risk of breast cancer can be predicted on the basis of a person’s genetic variants. The goal of this study is to evaluate the discriminatory power of common genetic variants in breast cancer risk estimation. We conducted a retrospective case-control study drawing from an existing personalized medicine data repository. We collected variables that predict breast cancer risk: 153 high-frequency/low-penetrance genetic variants, reflecting the state-of-the-art GWAS on breast cancer, mammography descriptors and BI-RADS assessment categories in the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We trained and tested naïve Bayes models by using these predictive variables. We generated ROC curves and used the area under the ROC curve (AUC) to quantify predictive performance. We found that genetic variants achieved comparable predictive performance to BI-RADS assessment categories in terms of AUC (0.650 vs. 0.659, p-value = 0.742), but significantly lower predictive performance than the combination of BI-RADS assessment categories and mammography descriptors (0.650 vs. 0.751, p-value < 0.001). A better understanding of relative predictive capability of genetic variants and mammography data may benefit clinicians and patients to make appropriate decisions about breast cancer screening, prevention, and treatment in the era of precision medicine.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978706 (24 March 2016); doi: 10.1117/12.2217030
Show Author Affiliations
Yirong Wu, Univ. of Wisconsin-Madison (United States)
Craig K. Abbey, Univ. of California, Santa Barbara (United States)
Jie Liu, Univ. of Washington (United States)
Irene Ong, Univ. of Wisconsin-Madison (United States)
Peggy Peissig, Marshfield Clinic (United States)
Adedayo A. Onitilo, Marshfield Clinic (United States)
Jun Fan, Univ. of Wisconsin-Madison (United States)
Ming Yuan, Univ. of Wisconsin-Madison (United States)
Elizabeth S. Burnside, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?