Being diagnosed with cancer can be frightening, especially not knowing what’s ahead. Not even physicians can answer with certainty questions like, “How fast is my tumor growing? How will it respond to therapy?”
Thomas Yankeelov, Ph.D., hopes to change that, by making the prediction of the growth of tumors during therapy as familiar and scientific as forecasting the weather. Yankeelov, a computational scientist, was recruited as a CPRIT Established Investigator and heads a new Center for Computational Oncology at The University of Texas at Austin. It’s the only center of its kind in the U.S.
“The overall goal of our research is to develop tumor-forecasting methods by integrating advanced imaging technologies with other patient-specific data,” said Yankeelov, who was a professor at Vanderbilt University before coming to Austin in early 2016. “We want to be able to predict tumor growth and optimize therapy for each patient.”
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Being diagnosed with cancer can be frightening, especially not knowing what’s ahead. Not even physicians can answer with certainty questions like, “How fast is my tumor growing? How will it respond to therapy?”
Thomas Yankeelov, Ph.D., hopes to change that, by making the prediction of the growth of tumors during therapy as familiar and scientific as forecasting the weather. Yankeelov, a computational scientist, was recruited as a CPRIT Established Investigator and heads a new Center for Computational Oncology at The University of Texas at Austin. It’s the only center of its kind in the U.S.
“The overall goal of our research is to develop tumor-forecasting methods by integrating advanced imaging technologies with other patient-specific data,” said Yankeelov, who was a professor at Vanderbilt University before coming to Austin in early 2016. “We want to be able to predict tumor growth and optimize therapy for each patient.”
Being able to make predictions about how a patient’s unique tumor will respond to therapy will not only improve a patient’s chance of survival, he believes, but also avoid side effects from unnecessary treatment.
Yankeelov began enrolling breast cancer patients in Austin in a clinical trial in October 2016. For the type of breast cancer his team is studying, therapy is given before surgery to shrink or even eliminate tumors. Yankeelov is especially interested in being able to predict a patient’s response to this pre-surgical treatment, called neoadjuvant therapy, to figure out which patients might have their disease eliminated by this initial therapy.
The study involves non-invasive, advanced magnetic resonance imaging (MRI) techniques. Patients undergo MRI imaging before treatment begins and after the first cycle of therapy. MRI data is used to calibrate a biophysical model that then makes a prediction of how the tumor will respond to therapy. The prediction will be compared with patient outcomes. If Yankeelov and his colleagues can build a model that reliably and accurately predicts tumor development, then it can be used to compute the best way to deliver therapy for each patient.
After developing the predictive models in an academic setting at Vanderbilt, Yankeelov is excited to be able to take them into clinics where patients already go for imaging.
“One of the major reasons our group moved to Austin was to be able to implement these ideas in a community setting, where most patients get their care,” he said. “If our methods prove successful, then the barrier between the bench and the bedside is dramatically lowered.”
Yankeelov hopes to expand his forecasting methods to other cancers that also benefit from neoadjuvant therapy, including brain cancer and pancreatic and rectal cancers.
Yankeelov studied mathematics at the University of Louisville and received two master’s degrees in mathematics and physics from Indiana University. He received a Ph.D. in biomedical engineering from the State University of New York, Stony Brook, before going to Vanderbilt University as a postdoctoral fellow in cancer imaging. He joined the faculty there in 2008.
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