UTSA professor's study describes new way to predict tumor growth

Artist's illustration of cancer attacking a human cell

Artist's illustration of cancer attacking a human cell.

(April 17, 2017) -- A new study by Yusheng Feng, professor of mechanical engineering and director of Center for Simulation, Visualization, and Real-Time Prediction at The University of Texas at San Antonio (UTSA), describes an algorithm that can predict the growth of cancerous tumors, which could help medical professionals judge the best treatment options for patients.

Feng first began researching cancer in 2002, predicting the outcomes of cancer treatments that utilize laser technology.

"In that project, we were using the heat of a laser to kill the cancer cells of the tumor," he said. "We had to use a computer simulation to show the amount of heat we were going to use and for how long, so we didn't damage any non-cancerous tissue."

In this project, Feng learned just how beneficial computer simulations can be when approaching treatments, especially cancer treatments, which regularly require surgery.

"One of the biggest advantages you can give a doctor and their patient is knowing how fast a tumor is growing and which treatment options are effective," he said. "This helps them to make the decision of not just when to treat someone, but also how to treat them."

Feng collaborated with colleagues at The University of Texas at Austin and the MD Anderson Cancer Center to create a novel algorithm described in the study. It takes into account major biological events in the tissue and cells of the patient, as well as signals inside a cell, among dozens of other factors. As a result, the algorithm is applicable to all types of cancers, as long as the relevant biological information is given.

"Outcome prediction is always good especially when it is reliable," he said. "And knowing the outcome of the treatment can be very beneficial."

Feng has plans to apply the algorithm to a computer program that can aid medical professionals in judging which treatments, if any, are appropriate for a patient's tumor based on how slowly or quickly it's growing. Moreover, the computer program may help to evaluate targeted therapies based on the prediction.

"Tumor cells are nothing but normal cells out of control that have migrated to the wrong place," he said. "That's why cancer is so hard to treat: it's your own cells."

UTSA is ranked among the top 400 universities in the world and among the top 100 in the nation, according to Times Higher Education.

- Joanna Carver


Read Yusheng Feng's study, "A fully coupled space-time multiscale modeling framework for predicting tumor growth."

Learn more about the UTSA Department of Mechanical Engineering.

Learn more about the UTSA Center for Simulation, Visualization, and Real-Time Prediction.

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