A new study is showing yet another way— and potentially improving existing practices for predicting breast cancer risk.
The study, which was published Tuesday in Radiology, a peer-reviewed journal, found AI algorithms outperformed the standard clinical risk model for predicting the five-year risk for
Risk models like the Breast Cancer Surveillance Consortium (BCSC) clinical risk score, which use self-reported and other patient information including age, family history and more, are typically used to calculate a woman's risk of breast cancer.
"Clinical risk models depend on gathering information from different sources, which isn't always available or collected," lead researcher Dr. Vignesh A. Arasu, a research scientist and practicing radiologist at Kaiser Permanente Northern California, said in a news release. "Recent advances in AI deep learning provide us with the ability to extract hundreds to thousands of additional mammographic features."
In the retrospective study, thousands of mammograms were analyzed, and risk scores for breast cancer over a five-year period were generated by five AI algorithms. Those scores were then compared to each other and the BCSC clinical risk score.
"All five AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years," Arasu said. "This strong predictive performance over the five-year period suggests AI is identifying both missed cancers and breast tissue features that help predict future cancer development."
While some institutions are already using AI to help detect cancer on mammograms, these findings suggest AI can be a vital tool in helping with a patient's future risk score — which takes seconds for AI to generate, according to the release.
"AI for cancer risk prediction offers us the opportunity to individualize every woman's care, which isn't systematically available," Arasu said. "It's a tool that could help us provide personalized, precision medicine on a national level."
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