OAK BROOK, Ill., Nov. 30, 2020 /PRNewswire/ — Researchers at Massachusetts General Hospital (MGH) have developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient’s risk for developing breast cancer with greater accuracy than traditional risk assessment tools. Results of the study are being presented at the annual meeting of the Radiological Society of North America (RSNA).
“Traditional risk assessment models do not leverage the level of detail that is contained within a mammogram,” said Leslie Lamb, M.D., M.Sc., breast radiologist at MGH. “Even the best existing traditional risk models may separate sub-groups of patients but are not as precise on the individual level.”
Currently available risk assessment models incorporate only a small fraction of patient data such as family history, prior breast biopsies, and hormonal and reproductive history. Only one feature from the screening mammogram itself, breast density, is incorporated into traditional models.
“Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman’s mammogram?” said senior author Constance D. Lehman, M.D., Ph.D., division chief of breast imaging at MGH. “Every woman’s mammogram is unique to her just like her thumbprint. It contains imaging biomarkers that are highly predictive of future cancer risk, but until we had the tools of deep learning, we were not able to extract this information to improve patient care.” [Read more…]