Upcoming Event: Babuška Forum

Actionable digital twins for patients who are not average

Tom Yankeelov, Principal Faculty, Oden Institute

Abstract

Over the last decade our team has developed a set of partial differential equations that capture key features of tumor dynamics related to cell migration, proliferation, treatment response, and tissue mechanical properties. These models can be calibrated with widely-available medical imaging data to accurately predict the spatio-temporal changes of solid tumors in response to both radiation and systemic therapies. We will briefly summarize those results, and then show how these models can power digital twins to identify alternative therapeutic regimens that may outperform the standard-of-care interventions.

Biography

Tom Yankeelov received an MA in Applied Mathematics and an MS in Physics from Indiana University, before completing the PhD in Biomedical Engineering at SUNY @ Stony Brook.  He had a post-doc position at the Vanderbilt University Institute of Imaging Science and climbed the ranks to Full Professor in 2010.  He then joined the faculty at The University of Texas at Austin in 2016 where he is now the Moncrief Chair of Computational Oncology and Professor of Biomedical Engineering and Diagnostic Medicine.  Dr. Yankeelov is the founding Director of the Center for Computational Oncology, and also serves as co-Director for the Quantitative Oncology Research Program within the Livestrong Cancer Institutes at UT Austin.  He is also an Adjunct Professor of Imaging Physics at MD Anderson Cancer Center.  The overall goal of Dr. Yankeelov's research is to develop tumor forecasting methods by integrating advanced imaging technologies with predictive models of tumor growth to optimize therapy on a patient-specific basis.  This is accomplished by dividing his efforts into approximately equal parts mathematical modeling, pre-clinical development, and implementation in clinical trials.