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Shepherd Research Lab Blog

Researchers Use Deep Learning to Predict Breast Cancer Risk

“Conventional methods of breast cancer risk assessment using clinical risk factors haven’t been that effective,” says study lead author John A. Shepherd, PhD, professor and researcher in the Population Sciences in the Pacific Program (Epidemiology) at the University of Hawaii Cancer Center in Honolulu. “We thought that there was more in the image than just breast density that would be useful for assessing risk.”

For the new study, Shepherd and colleagues used a data set of more than 25,000 digital screening mammograms from 6,369 women who participated in screening mammography. More than 1,600 of the women developed screening-detected breast cancer, and 351 developed interval invasive breast cancer.

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