A practical computational platform for systematically investigating alternative treatment schedules for cancer
Tom Yankeelov, PhD, Professor of Biomedical Engineering and Diagnostic Medicine
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 completed his post-doc 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 W. A. “Tex” 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 UT Austin Cancer Center. 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 time into approximately equal parts mathematical modeling, pre-clinical development, and implementation in clinical trials. These efforts have resulted in 284 scientific publications, nearly 400 conference abstracts, and > 260 presentations at institutions around the world. In 2026, Dr. Yankeelov won the UT President’s Research Impact award. He is also an accomplished classroom teacher, having won the 2025 Friar Centennial Teaching Award at UT Austin.
Over the last decade our team has developed a set of partial differential equations that capture key features of tumor growth and treatment response related to tumor 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 for various solid tumors, and then show how these models power digital twins to systematically identify alternative therapeutic regimens that may outperform the current standard-of-care interventions.