September 18th, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Souptik Barua (ELEC)
Please indicate interest, especially if you want lunch, here.
Abstract:
Immunotherapy and radiation therapy are two of the most prominent strategies used to treat cancer. While both these treatments have succeeded in removing the disease in many patients and cancer types, they are known to not work well for all patients, sometimes even leading to adverse side effects. There is thus a critical need to be able to predict how patients might respond to these therapies and accordingly design optimal treatment plans. In this talk, I describe data-driven frameworks that leverage structure in two types of cancer imaging data (multiplexed immunofluorescence or mIF images, and CT scans) to predict clinical outcomes of interest. In the case of mIF images, I use ideas from spatial statistics and functional data analysis to design metrics that describe the spatial distributions of immune cells in a tumor. i then show that these metrics can predict outcomes such as survival and risk of progression in pancreatic cancer. In the case of CT scans, I use a functional data analysis technique to capture temporal changes in CT scans captured at multiple time points, and use that to predict two clinical outcomes a) if patients undergoing radiation treatment are likely to have a complete response, b) if patients are going to develop long-term side effects of radiation treatment such as osteoradionecrosis.