Share Email Print
cover

Proceedings Paper • new

Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction
Author(s): Giacomo Nebbia; Aly Mohamed; Ruimei Chai; Bingjie Zheng; Margarita Zuley; Shandong Wu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Breast cancer risk prediction refers to the task of predicting whether a healthy patient is likely to develop breast cancer in the future. Breast density and parenchymal texture features are well-known imaging-based breast cancer risk markers that can be qualitatively/visually assessed by radiologists or even quantitatively measured by computerized software. Recently, deep learning has emerged as a promising strategy to solve tasks in a variety of classification and prediction scenarios, including breast imaging. Building on this premise, we propose a deep learning-based modeling method for breast cancer risk prediction in a case-control setting purely using prior normal screening mammogram images. In addition, considering the fact that clinical statistics shows that the upper outer quadrant is the most common site of origin for breast cancer, we designed a simple experiment on 226 patients (a total of 1,632 images) to explore the concept of localized breast cancer risk prediction. We built two deep learning models with the same settings but fed one with the top halves of the mammogram images (corresponding to the outer portion of a breast) and the other with the bottom halves (corresponding to the inner portion of a breast). Our preliminary results showed that the top halves have a higher prediction performance (AUC=0.89) than the bottom halves (AUC=0.69) in predicting the case/control outcome. This indicates a relation between localized imaging features extracted from a sub-region of the full mammogram images and the underlying risk of developing breast cancer in this specific sub-region.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502P (13 March 2019); doi: 10.1117/12.2512943
Show Author Affiliations
Giacomo Nebbia, Univ. of Pittsburgh (United States)
Aly Mohamed, Univ. of Pittsburgh (United States)
Ruimei Chai, Liaoning Cancer Hospital & Institute (China)
Bingjie Zheng, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou Univ. (China)
Margarita Zuley, Univ. of Pittsburgh (United States)
Shandong Wu, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

© SPIE. Terms of Use
Back to Top