In this episode of The OncoZine Brief, Peter Hofland, Ph.D and Sonia Portillo talk with Thomas Yankeelov, Ph.D, a computational biomedical engineer who came to The University of Texas at Austin from Vanderbilt University, where he served as the Ingram Professor of Cancer Research; professor of radiology and radiological sciences, physics, biomedical engineering and cancer biology; and director of cancer imaging research. He also served as a co-leader of the Host-Tumor Interactions Research Program for the Vanderbilt-Ingram Cancer Center.
Thomas Yankeelov serves as Director of Cancer Imaging Research in the LIVESTRONG Cancer Institutes of the Dell Medical School. He holds the W.A. “Tex” Moncrief Jr., Simulation-Based Engineering and Sciences Professorship II – Computational Oncology and leads the Tumor Modeling Group in the university’s Institute for Computational Engineering and Sciences.
Yankeelov is the recipient of a distinguished US $ 6 million recruitment grant from the Cancer Prevention and Research Institute of Texas (CPRIT). He was The University of Texas’ first faculty member to hold positions in both the engineering and medical schools.
Yankeelov clinical research focuses on improving patient care by employing advanced imaging methods for the early identification, assessment and prediction of tumors and their response to therapy.
He has developed successful tumor-forecasting methods by combining imaging technologies with patient-specific data to build predictive, multi-scale biophysical models of tumor growth. His research emphasizes the importance of offering personalized therapies to cancer patients. Yankeelov is a fellow of the American Institute of Medical and Biological Engineers and has served on the editorial boards of scientific publications.
The overall goal of Yankeelov’s clinical research is to improve patient care by employing advanced imaging methods for the early identification, assessment, and prediction of tumors’ response to therapy. In order todo that, he has developed tumor forecasting methods by integrating advanced imaging technologies with patient-specific data and builds predictive, multi-scale biophysical models of tumor growth with the purpose of optimizing therapies for the individual cancer patient.
In this interview Yankeelov, Hofland and Portillo ask about computational oncology, what this is and how it fits in cancer treatment. They also ask about oncology models, what these models are trying to tell us, and how are they developed and how using these cancer models leads to a different way in looking at cancer.
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