Screening mammography has been shown effective in early detection of breast cancer and in reducing mortality. However, controversies and challenges still remain, with primary concerns on personal breast cancer risk prediction from mammographic parenchymal markers, high recall and benign biopsy rates, and improving radiologists? clinical reading practices. Computerized methods have been developed in these regards, with the goal of providing computer assistance to radiologists in making clinical decisions. While successful, the accuracy of these methods is subject to appropriate data representation (i.e., image features) that requires strong feature engineering. A newly emerged artificial intelligence technique, called deep learning, represents a breakthrough in machine learning paradigms, and has revolutionized computer image analysis and many other applications in the past few years. Breast cancer screening yields a huge amount of mammogram data that requires in-depth interpretation to improve current clinical workup. The goal of this study is to develop and optimize a convolutional neural network (CNN)-based computational approach to improve mammographic imaging trait identification, analysis, and interpretation and to use this approach to address accurate breast cancer risk prediction and reduce false recall rates. This study will be the first to examine the effects of the revolutionary deep learning technique on performing in-depth interpretation of big screening mammogram data, aimed at improving clinical practice. The new risk biomarkers will contribute to providing more accurate risk prediction than currently available. The recall-decision model will help reduce false recalls (associated with potential benign biopsy results), and better understand radiologists? reading behaviors. Overall, the CNN-based approach will optimize the clinical utility of screening mammography and has a high likelihood to translate to the clinic for breast cancer screening.
): We propose to apply a revolutionary deep learning approach to mine a big data of digital mammograms for improving mammogram data analysis and interpretation. This study will provide a computer-aided deep learning toolkit for improving breast cancer risk prediction and for aiding individual radiologists in their clinical decision-making on recalls. By introducing this newly emerged powerful machine learning technique to the clinical side of breast cancer screening, it will contribute to improving personal breast cancer risk prediction and reducing unnecessary recalls. The study will optimize the clinical utility of screening mammograms and has a high likelihood to translate to the clinic for breast cancer screening.
| Aboutalib, Sarah S; Mohamed, Aly A; Berg, Wendie A et al. (2018) Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clin Cancer Res 24:5902-5909 |
