"Rather than taking a one-size-fits-all approach, we can personalize screening around a woman's risk of developing cancer," says Barzilay, senior author of a new paper about the project out recently in Radiology. "For example, a doctor might recommend that one group of women get a mammogram every other year, while another higher-risk group might get supplemental MRI screening." Barzilay is the Delta Electronics Professor at CSAIL and the Department of Electrical Engineering and Computer Science at MIT and a member of the Koch Institute for Integrative Cancer Research at MIT.

Despite significant advancements in genetics and modern imaging technology, for the vast majority of breast cancer patients, the diagnosis catches them by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, anxiety and uncertain outcomes. Therefore, identifying patients at risk before the disease develops has been a central pillar to breast cancer research and effective early detection programs. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer in the future.

Despite major advances in genetics and modern imaging, the diagnosis catches most breast cancer patients by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, uncertain outcomes, and more medical expenses. As a result, identifying patients has been a central pillar of breast cancer research and effective early detection. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future.