Some of the AI algorithms excelled at predicting patients at high risk of interval cancer, which is often aggressive and may require a second reading of mammograms, supplementary screening or short-interval follow-up imaging. When evaluating women with the highest 10% risk as an example, AI predicted up to 28% of cancers compared to 21% predicted by BCSC.
Even AI algorithms trained for short time horizons (as low as 3 months) were able to predict the future risk of cancer up to five years when no cancer was clinically detected by screening mammography. When used in combination, the AI and BCSC risk models further improved cancer prediction.
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“We’re looking for an accurate, efficient and scalable means of understanding a women’s breast cancer risk,” Dr. Arasu said. “Mammography-based AI risk models provide practical advantages over traditional clinical risk models because they use a single data source: the mammogram itself.”
Dr. Arasu said some institutions are already using AI to help radiologists detect cancer on mammograms. A person’s future risk score, which takes seconds for AI to generate, could be integrated into the radiology report shared with the patient and their physician.
“AI for cancer risk prediction offers us the opportunity to individualize every woman’s care, which isn’t systematically available,” he said. “It’s a tool that could help us provide personalized, precision medicine on a national level.”
“Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study.” Collaborating with Dr. Arasu were Laurel A. Habel, Ph.D., Ninah S. Achacoso, M.S., Diana S. M. Buist, Ph.D., Jason B. Cord, M.D., Laura J. Esserman, M.D., Nola. M. Hylton, Ph.D., M. Maria Glymour, Sc.D., John Kornak, Ph.D., Lawrence H. Kushi, Sc.D., Don A. Lewis, M.S., Vincent X. Liu, M.D., Caitlin M. Lydon, M.P.H., Diana L. Miglioretti, Ph.D., Daniel A. Navarro, M.D., Albert Pu, M.S., Li Shen, Ph.D., Weiva Sieh, M.D., Ph.D., Hyo-Chun Yoon, M.D., Ph.D., and Catherine Lee, Ph.D.
In 2023, Radiology is celebrating its 100th anniversary with 12 centennial issues, highlighting Radiology’s legacy of publishing exceptional and practical science to improve patient care.
Radiology is edited by Linda Moy, M.D., New York University, New York, N.Y., 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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