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

Dooman Arefan; Bingjie Zheng; David J. Dabbs; Rohit Bhargava; Shandong Wu

doi:10.1117/12.2513013

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15 March 2019 Multi-space-enabled deep learning of breast tumors improves prediction of distant recurrence risk

Dooman Arefan, Bingjie Zheng, David J. Dabbs, Rohit Bhargava, Shandong Wu

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Proceedings Volume 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications; 109540L (2019) https://doi.org/10.1117/12.2513013
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

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Dooman Arefan, Bingjie Zheng, David J. Dabbs, Rohit Bhargava, and Shandong Wu "Multi-space-enabled deep learning of breast tumors improves prediction of distant recurrence risk", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540L (15 March 2019); https://doi.org/10.1117/12.2513013

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