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Proceedings Paper

Multi-space-enabled deep learning of breast tumors improves prediction of distant recurrence risk
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Paper Abstract

In this study, we proposed a multi-space-enabled deep learning modeling method for predicting Oncotype DX recurrence risk categories from digital mammogram images on breast cancer patients. Our study included 189 estrogen receptor-positive (ER+) and node-negative invasive breast cancer patients, who all have Oncotype DX recurrence risk score available. Breast tumors were segmented manually by an expert radiologist. We built a 3- channel convolutional neural network (CNN) model that accepts three-space tumor data: the spatial intensity information and the phase and amplitude components in the frequency domain. We compared this multi-space model to a baseline model that is based on sorely the intensity information. Classification accuracy is based on 5- fold cross-validation and average area-under the receiver operating characteristics curve (AUC). Our results showed that the 3-channel multi-space CNN model achieved a statistically significant improvement than the baseline model.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540L (15 March 2019); doi: 10.1117/12.2513013
Show Author Affiliations
Dooman Arefan, Univ. of Pittsburgh (United States)
Bingjie Zheng, Affiliated Cancer Hospital of Zhengzhou Univ. (China)
David J. Dabbs, Univ. of Pittsburgh (United States)
Rohit Bhargava, Univ. of Pittsburgh (United States)
Shandong Wu, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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