Past Event: Oden Institute Seminar

Towards n = 1 clinical trials for clinical oncology

Tom Yankeelov, The Oden Institute

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.  The imaging methods we use extend from time-resolved microscopy to longitudinal MRI and PET scans, while the modeling methods rely mostly on ordinary and partial differential equations. In this presentation, we will focus on how quantitative 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 in predicting and optimizing the response of breast cancer to pre-surgical therapy. The long-term goal of this set of studies is to provide a rigorous methodology that is practical enough for designing and updating therapeutic interventions on a patient-specific basis. 

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 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 Moncrief Chair of Computational Oncology and Professor of Biomedical Engineering, Diagnostic Medicine, and Oncology.  Tom 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 his team 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.