AUSTIN (KXAN) — Through complex equations and science, meteorologists are able to anticipate where deadly disasters will hit. Researchers at the University of Texas Austin want to do the same thing, with cancer. It’s a new field called ‘computational oncology.’
Dr. Thomas Yankeelov was brought to UT to help develop a center for computational oncology, helping to fight cancer in a new way.
“Computational oncology is a very new field, there’s maybe half a dozen or so centers around the country that are really blazing the path in this field,” Dr. Yankeelov, a professor in the Department of Biomedical Engineering said. “The idea is to use mathematics and computation, meaning computers, to predict how tumors are going to grow and respond.”
Dr. Yankeelov believes by making early predictions on how a patient’s unique tumor will respond to therapy, they can improve survival rates and avoid side effects for unnecessary treatments.
They’re looking to the field of meteorology for guidance.
“There are these things called Navier-Stokes equations that are the backbone for meteorology and weather prediction. We’re trying to come up with the Navier-Stokes equation for how tumors grow, so that we can predict how a tumor can grow and respond,” said Dr. Yankeelov.
He says in the same way that meteorology makes measures on pressure, wind speed and temperature, they want to make measurements on vascularity, metabolism and proliferation, to predict how tumors are going to grow.
Amanda Meriwether is a student taking computational oncology with Dr. Yankeelov.
“I feel like this research, developing tumor prediction models, has a much smaller timescale so we can start having a greater impact on people, sooner,” Meriwether said.
Dr. Yankeelov is currently doing a clinical trial with breast cancer patients from Seton and Texas Oncology. The researchers will use their approach to predict how breast cancer patients with localized tumors will respond to chemotherapy, radiation therapy or hormone therapy before surgery.
In a previous clinical trial with 42 people, Dr. Yankeelov’s models were 88 percent accurate in correctly predicting patient outcomes.
Data collected over the next two years will not be used to treat patients in this study. If successful, the researchers will conduct a follow-up study in which they share data with physicians who could use the information to help guide therapy.
“In the neighborhood of three to five years we’ll have an idea of whether or not this is going to make a real difference,” said Dr. Yankeelov. “I have to tell you, I’m very biased! I think this is the way to do it. I think this will work.”