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Deep learning can distinguish recalled-benign mammograms from malignant and negative images

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Brian Christy
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Bottom Line: An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.

Author: Shandong Wu, PhD, assistant professor of radiology, biomedical informatics, bioengineering, intelligent systems, and clinical and translational science, and director of the Intelligent Computing for Clinical Imaging lab in the Department of Radiology at the University of Pittsburgh, Pennsylvania

Background: "In order to catch breast cancer early and help reduce mortality, mammography is an important screening exam; however, it currently suffers from a high false recall rate," said Wu.

"These false recalls result in undue psychological stress for patients and a substantial increase in clinical workload and medical costs.

How the Study Was Conducted: Wu and colleagues studied whether a technique in artificial intelligence called deep learning could be applied to analyze a large set of mammograms in order to distinguish images from women with a malignant diagnosis, images from women who were recalled and were later determined to have benign lesions (false recalls), and images from women determined to be breast cancer-free at the time of screening.

"The assumption is that there may be some nuanced imaging features associated with some mammogram images that could lead to a false/unnecessary recall when the images are interpreted by human radiologists, and our goal is to utilize a deep learning CNN-based method to build a computer toolkit to identify those potential mammogram images," Wu said.

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