Featured Scientist

Dr. Thomas Yankeelov

Thomas Yankeelov, Ph.D., joined the faculty of The University of Texas at Austin in January of 2016 as the W.A. “Tex” Moncrief Professor of Computational Oncology and Director of the Center for Computational Oncology at the Institute for Computational and Engineering Sciences. He is also a Professor of Biomedical Engineering and Internal Medicine, and Director of Cancer Imaging Research for the Livestrong Cancer Institutes. He received an M.A. in Applied Mathematics and an M.S. in Physics from Indiana University in 1998 and 2000, respectively.

His doctorate is in Biomedical Engineering from SUNY at Stony Brook, where he completed his dissertation at Brookhaven National Laboratory in 2003. After taking a post-doctoral fellowship at Vanderbilt University, he was promoted to Assistant Professor of Radiology in 2005 and achieved the rank of Full Professor of Radiology, Biomedical Engineering, Physics, and Cancer Biology in 2014. His research program focuses on the development and application of methods for integrating in vivo imaging data with biomathematical and biophysical models of tumor growth and treatment response with the goal of being able to predict and optimize patient outcomes. When not working on these problems, Dr. Yankeelov and his wife (Dr. Margie Yankeelov, also a UT faculty member in the College of Fine Arts) are having a wonderful time exploring Austin with their two small children who are, just like your children, perfect.

The aims of the pre-clinical component of our research program are to develop, critically evaluate, and validate in vivo image data acquisition and analysis methods to yield quantitative (surrogate) biomarkers of the response of tumors to specific treatments. The guiding hypothesis is that treatment type determines which imaging metrics are the most sensitive to early response, thereby enabling specific treatment classes to be paired with specific imaging approaches so that the most sensitive imaging biomarkers can be selected when designing clinical trials. With this approach, imaging techniques could be tailored to individual treatment regimes, consequently having a direct impact on personalized medicine.

The goal of our clinical efforts is to predict the eventual response of cancer patients by characterizing changes in a set of quantitative imaging parameters from baseline to the post-one cycle of therapy time point. The ability to determine patient response at this early time point (as soon as one week after therapeutic initiation) would allow clinicians to tailor therapy for an individual patient based on each patient’s response to a particular agent. The long term goal of this effort is to provide the cancer community with practical data acquisition and analysis protocols that facilitate the translation of advanced imaging technologies into patient management and clinical trials.