Clinical Trials For Patients Who Are Not Average By Tom Yankeelov, Ph.D. W.A. "Tex" Moncrief Chair of Computational Oncology; Director, Center for Computational Oncology, Oden Institute for Computational Engineering and Sciences; Director, Cancer Imaging Research, Livestrong Cancer Institutes; Co-leader, Quantitative Oncology Research Program, Livestrong Cancer Institutes; Adjunct Professor of Imaging Physics, MD Anderson Cancer Center; Professor of Biomedical Engineering, Diagnostic Medicine, Oncology. U.Texas, Austin, Texas Wednesday, January 17, 2024, 12:00-1:00 PM EST Register at https://rosaandco.com/webinars Abstract: Our lab is focused on developing tumor forecasting methods by integrating advanced imaging technologies with mathematical models to predict tumor growth and treatment response. In this presentation, we will focus on how quantitative magnetic resonance imaging (MRI) data can be employed to calibrate mathematical models built on first-order effects related to well-established "hallmarks" of cancer including proliferation, migration/invasion, vascular status, and drug-related tumor growth inhibition and cell death. In particular, we will present some of our recent results through four vignettes focusing on breast and brain cancer: 1) incorporating patient-specific data into mechanism-based mathematical models, 2) predicting and optimizing outcomes via patient-specific digital twins, 3) guiding interventions through applications of optimal control theory, and 4) updating predictions through data assimilation. The long-term goal of this set of studies is to provide a rigorous methodology that is practical enough for predicting--and optimizing--therapeutic interventions on a patient-specific basis.
