The model also enabled identification of women at high risk for breast cancer. Women identified by the combined model as having the 10% highest combined risk accounted for 44.1% of interval cancers and 33.7% of long-term cancers.
Using AI to identify a women’s breast cancer risk from a single mammogram will not only result in earlier cancer detection but can also improve the strain on the health care system due to the worldwide shortage of specialized breast radiologists.
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“Current state-of-the-art clinical risk models require multiple tests such as blood work, genetic testing, mammogram and filling out extensive questionnaires, all of which would substantially increase the workload in the screening clinic,” Dr. Lauritzen said. “Using our model, risk can be assessed with the same performance as the clinical risk models but within seconds from screening and without introducing overhead in the clinic.”
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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