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13 March 2019 Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction
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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.
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Giacomo Nebbia, Aly Mohamed, Ruimei Chai, Bingjie Zheng, Margarita Zuley, and Shandong Wu "Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502P (13 March 2019); https://doi.org/10.1117/12.2512943
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