"This would allow us to use a woman's individual risk to determine how frequently she should be monitored," Dr. Shepherd said. "Lower-risk women might not need to be monitored with mammography as often as those with a high risk of breast cancer."
The deep learning model also has promise in supporting decisions about additional imaging with MRI and other modalities. Dr. Shepherd said that women in the high-risk deep learning group who also have dense breasts and are at a higher risk for interval cancers may benefit most from a monitoring strategy that includes supplemental imaging that retains sensitivity in dense breasts such as MRI, ultrasound and molecular imaging. Interval cancers usually have more aggressive tumor biology and are typically discovered at an advanced stage.
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Along with other recent research, the new study supports a role for AI in combination with clinical risk factors in breast cancer risk assessment.
"By ranking mammograms in terms of the probability of seeing cancer in the image, AI is going to be a powerful second reading tool to help categorize mammograms," Dr. Shepherd said.
The researchers are planning to replicate the study in Native Hawaiian and Pacific Islander women, two groups that have been underrepresented in breast cancer research. They also want to extend the work beyond cancer risk to look at the risk of different grades of breast cancer, from least to most aggressive.
"Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women." Collaborating with Dr. Shepherd were Xun Zhu, Ph.D., Thomas K. Wolfgruber, Ph.D., Lambert Leong, M.S., Matthew Jensen, B.S., Christopher Scott, M.S., Stacey Winham, Ph.D., Peter Sadowski, Ph.D., Celine Vachon, Ph.D., and Karla Kerlikowske, M.D.
Radiology is edited by David A. Bluemke, M.D., Ph.D., University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, and owned and published by the Radiological Society of North America, Inc.
About RSNA
RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois.
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