A deep learning computer model developed by researchers at Massachusetts General Hospital (MGH) was able to identify subtle information in breast cancer images that could help better predict a woman's chances of developing the disease. Currently, the main methods of judging individual risk include checking for a family history of cancer, evaluating any biopsied tissue and noting whether they've given birth to a child. Screening mammograms--recommended annually by the American Cancer Society for women between the ages of 45 and 54--are typically used by oncologists to measure the density of the breast. "Why should we limit ourselves to only breast density when there is such rich digital data embedded in every woman's mammogram?" said Constance Lehman, M.D., Ph.D., MGH's division chief of breast imaging and senior author of a paper presented at the annual meeting of the Radiological Society of North America. "Every woman's mammogram is unique to her just like her thumbprint," Lehman said.