Dr. David Hormuth (University of Texas at Austin)
Abstract: There is a long history of developing phenomenological and mechanistic mathematical models of tumor growth and response. While these models can be used to generate testable hypothesis or investigate a host of biological scenarios, it is not until recently that these models have had the potential to be personalized for individual subjects. A limitation to existing models is that they often contain model parameters which are impossible or impractical to determine for individual patients. Through non-invasive quantitative magnetic resonance imaging (MRI) methods, such as dynamic contrast enhanced MRI and diffusion weighted MRI, we are now able to quantify (in 3D) tumor properties such as perfusion, vascularity, proliferation, and cellularity for individual tumors.
These measurables can be used to initialize and calibrate models of tumor growth and response. In this talk, I will present an application of using quantitative MRI data to inform a clinical model of tumor growth and response to radiotherapy. For this scenario, we will evaluate the accuracy of tumor growth predictions at the voxel and global levels. With further development, we hypothesize that these subject-specific modeling techniques could deliver the opportunity to predict patient response early in the course of therapy, simulate patient-specific treatment regimens, and eventually optimize or adapt therapy for individual tumors.
Bio: David A. Hormuth, II Ph.D. is a Research Associate in the Center for Computational Oncology in the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin. He received his Ph.D. from Vanderbilt University and was a cancer imaging trainee in the Vanderbilt University Institute for Imaging Science. His current research interests are the integration of medical imaging data with mathematical and computational techniques to predict response to radiotherapy, optimize therapeutic regimens, simulating radiopharmaceutical delivery, and translating pre-clinical efforts to clinical setting.