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.