“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” Wu said. As they reported in the American Association of Physicists in Medicine, both models consistently outperformed the simple measure of breast density, which today is the primary imaging marker for breast cancer risk. The team’s models demonstrated between 33% and 35% improvement over these existing models, based on metrics that incorporate sensitivity and specificity. Leveraging AWS tools, such as Amazon SageMaker, they used two different machine learning models to analyze the images for characteristics that could help predict breast cancer risk. Wu and his colleagues collected 452 de-identified normal screening mammogram images from 226 patients, half of whom later developed breast cancer and half of whom did not. A team of experts in computer vision, deep learning, bioinformatics, and breast cancer imaging are working together to develop a more personalized approach for patients undergoing breast cancer screening. In work funded through the PHDA-AWS collaboration, a research team led by Shandong Wu, an associate professor in the University of Pittsburgh Department of Radiology, is using deep-learning systems to analyze mammograms in order to predict the short‐term risk of developing breast cancer. Researchers from the University of Pittsburgh Medical Center ( UPMC), the University of Pittsburgh, and Carnegie Mellon University (CMU), who were already supported by the PHDA, received additional support from Amazon Research Awards to use machine learning techniques to study breast cancer risk, identify depression markers, and understand what drives tumor growth, among other projects. In August of 2019, the Pittsburgh Health Data Alliance (PHDA) and Amazon Web Services (AWS) announced a new collaboration to advance innovation in areas such as cancer diagnostics, precision medicine, electronic health records, and medical imaging.
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