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New Clinical Partnership Uses Advanced Imaging Methods to Fight Breast Cancer


In partnership with Seton Healthcare Family, Texas Oncology and Austin Radiological Association, The University of Texas at Austin has embarked on a two-year clinical study to test advanced imaging methods that could provide patients and physicians with new, valuable information on how to treat breast cancer in patients.

Thomas Yankeelov, who holds a joint faculty appointment in the university’s Cockrell School of Engineering and Dell Medical School, and his team of biomedical engineering and medical researchers are testing an approach that uses advanced magnetic resonance imaging (MRI) and analysis to make early predictions on how a specific patient’s tumor will eventually respond to therapy. The predictions could save valuable time for patients by assessing the efficacy of treatments and determining necessary alternatives earlier in the therapy process. The clinical study, which is the first to take place through the Dell Medical School, began on Oct. 1. The team plans to enroll 100 breast cancer patients from Seton and Texas Oncology.

The UT Austin team’s patient-specific computer model could improve overall patient response, prolong survival and avoid side effects from unnecessary treatments. Patients will be scanned with standard-of-care MRI imaging equipment at Seton, Austin Radiological Association and UT Austin.

“This public-private partnership allows us to take our research and bring it into the community,” said Yankeelov, who is also director of the Center for Computational Oncology within the Institute for Computational and Engineering Sciences. “This is a great example of engineers, scientists and physicians working together to find solutions, develop new methods and deliver therapies to cancer patients faster and more effectively.”

Yankeelov came to UT Austin through a $6 million recruitment grant from the Cancer Prevention and Research Institute of Texas. He also serves as director of cancer imaging research for the LIVESTRONG Cancer Institutes of the Dell Medical School, which seeks to rethink the range of cancer care, focusing on patients’ lives within a framework of patient-centered research. Yankeelov’s team includes Anna Sorace and Jack Virostko, both assistant professors of medicine at the Dell Med School.

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.

“Patients who are enrolled in the clinical trial will help demonstrate whether this new imaging protocol will make a difference for patients with breast cancer,” said Dr. Boone Goodgame, an oncologist at Seton, assistant professor at the Dell Medical School and UT Austin mechanical engineering alumnus (B.S. ME 1997). “In the future, with new algorithms designed by Dr. Yankeelov’s group, we may be able to tell after one dose of chemo if the cancer is going to respond.”

Traditional methods to evaluate cancer therapy often require painful biopsies and provide information at a slower rate. This mathematics-based approach, which relies on a combination of advanced MRI techniques, computer simulations and algorithms, allows experts to calculate how the body is responding to therapy before there are changes to the tumor’s size. The approach provides quantitative data on biological responses, including cell growth and how vessels are delivering blood.

In previous clinical trials conducted by Yankeelov and his team, models correctly predicted patient outcomes with an 88 percent accuracy rate for a group of 42 people.

“In this next round of testing, if we can improve that 88 percent to 95 percent in this larger patient population, then I think we’ll be in a position to have some serious conversations with physicians treating cancer patients,” Yankeelov said.

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.

This study is funded by the National Institutes of Health’s National Cancer Institute and the Cancer Prevention and Research Institute of Texas.

For information on how to enroll in the study, contact Marissa Canales, research group administrative support at the Cockrell School of Engineering, m.canales@austin.utexas.edu.