A new method of analyzing mammograms identified individuals at high risk of developing breast cancer more accurately than the standard, questionnaire-based method did.
The new method, powered by artificial intelligence, could help diagnose cancer earlier and guide recommendations for earlier screening, additional imaging or risk-reducing medications.
For the new study, the team built an algorithm based on artificial intelligence that can discern subtle differences in mammograms and help identify those women at highest risk of developing a new breast tumor over a specific timeframe. In addition to breast density, their machine-learning tool considers changes in other patterns in the images, including in texture, calcification and asymmetry within the breasts.
"Today, we don't have a way to know who is likely to develop breast cancer in the future based on their mammogram images," said co-author Debbie L. Bennett, MD, an associate professor of radiology and chief of breast imaging for the Mallinckrodt Institute of Radiology at WashU Medicine."What's so exciting about this research is that it indicates that it is possible to glean this information from current and prior mammograms using this algorithm.
The mammograms were conducted at academic medical centers and community clinics, demonstrating that the accuracy of the method holds up in diverse settings. Importantly, the algorithm was built with robust representation of Black women, who are usually underrepresented in development of breast cancer risk models. The accuracy for predicting risk held up across racial groups. Of the women screened through Siteman, most were white, and 27% were Black.
Jiang S, Bennett DL, Rosner BA, Tamimi RM, Colditz GA. Development and validation of a dynamic 5-year breast cancer risk model using repeated mammograms. JCO Clinical Cancer Informatics. Dec. 5, 2024.Shu Jiang, Debbie L. Bennett, Bernard A. Rosner, Rulla M. Tamimi, Graham A. Colditz.A new advanced artificial intelligence algorithm more accurately model how genes associated with specific autoimmune diseases are expressed and regulated and to identify additional genes of ...
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