Researchers in UT’s Tickle College of Engineering are using artificial intelligence and machine learning to provide treatment plans to breast cancer patients more quickly by analyzing pathology reports and other clinical records to determine how much breast cancer is in the body. Breast cancer is the second most common cancer in Tennessee and the second most common cancer among women in the United States.
After someone is diagnosed with breast cancer, doctors must determine the extent of the disease, if it has spread or not, and where it is located. This process is called staging. The stage describes how much cancer is present and determines how serious the cancer is and how best to treat it.
“It often takes humans up to two hours to stage one case of breast cancer, and now we can do it in one click,” said Xueping Li, a professor and Dan Doulet Faculty Fellow in the Department of Industrial and Systems Engineering, co-director of the Health Innovation Technology and Simulation Lab, and director of the Ideation Laboratory.