With the large number of women diagnosed and treated for breast cancer each year, the importance of studying recurrence has become increasingly evident as recurrence is the leading cause of death in breast cancer patients. To mitigate this, our group works to develop imaging platforms that can capture and establish metabolic and molecular signatures that report on a patient’s risk of developing recurrent disease. Specifically, we are interested in developing portable and low-cost technologies that can capture a tumor’s underlying behavior across a wide range of biological models to answer questions spanning tumors’ energetic profiles in animal models to patient-specific molecular signatures in patient biopsies.