This seminar is co-sponsored by the Herbert Irving Comprehensive Cancer Center (HICCC) and the Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID)

Title: Personalizing interventions through imaging-based digital twins

Abstract:

Our lab focuses on developing tumor forecasting methods by integrating medical imaging data with biology-based, mathematical models to predict tumor growth and treatment response on an individual patient basis. These data are acquired before therapy begins, and again at one or more time points during therapy. All model parameters that are not directly measured are calibrated to the longitudinal imaging data from a patient. Then, the personalized model is run forward to predict tumor size and other characteristics of the patient’s tumor at future times which can be directly tested against observation and, ultimately, guide intervention.  In this presentation, we will provide an overview of this effort through four vignettes focusing on 1) incorporating patient-specific data into biology-based mathematical models, 2) simulating outcomes via digital twins, 3) guiding interventions through optimal control theory, and 4) updating interventions through data assimilation. To tell a complete story we will focus on breast cancer, but also summarize our efforts in brain, cervical, and prostate cancer.

Speaker: Thomas Yankeelov, PhD, Director of Center for Computational Oncology, UT Austin

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 with 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 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.