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 discuss how quantitative magnetic resonance imaging data (MRI) can be employed to initialize and constrain 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. More specifically, we will present some of our recent results through four vignettes focusing on breast cancer: 1) incorporating patientspecific data into mechanism-based mathematical models, 2) simulating outcomes via patientspecific digital twins, 3) rigorously guiding interventions through optimal control theory, and 4) updating interventions through data assimilation. The long-term goal of this set of studies is to provide a rigorous--but practical--methodology that allows for optimizing, and adjusting in near real-time, therapeutic interventions on a patient-specific basis.

About the speaker: 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 under Dr. John Gore 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, Diagnostic Medicine, and Oncology.  Dr. Yankeelov is the founding Director of the Center for Computational Oncology, and also serves as co-Director for the Quantitative Oncology Research Program and Director of Cancer Imaging Research 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, multi-scale models of tumor growth to optimize therapy.  This is accomplished by dividing his efforts into approximately equal parts mathematical modeling, pre-clinical development, and implementation in clinical trials.

Research Interests: Cancer imaging, mathematical oncology, computational oncology, digital twins