Predicting Cancer Treatment Success

No two cancer patients respond identically to treatment. Some will be cured while others will see their cancer return, and physicians are at a loss to explain why. Now, using MRI imaging researchers have developed a mathematical model of tumor growth that identifies two factors that are predictive of cervical cancer treatment success: responsiveness to radiation and the ability to clear dead cells.


Ohio State University researchers used magnetic resonance imaging and a mathematical model to predict cancer recurrence. These images show decreasing tumor volume over a 5 week radiation course in a patient who was alive and cancer free 9 years later. Photo Credit: Dr. William Yuh and Dr. Nina Mayr.“This work gives us strategies to find out early on if the tumor does not respond to cancer therapy … and to adjust treatment to increase the chance of cure,” says, Nina Mayr, MD, radiation oncologist at Ohio State University and principal investigator of the study. The work was presented at the annual meeting of the American Association of Physicists in Medicine.


Currently, “little is known about the underlying biological mechanisms that govern the tumor response to radiation therapy,”  says Zhibin Huang, PhD,  a postdoctoral researcher at Ohio State University and lead author of the study. “We wanted to see if this imaging technology could find some early indica- tions of the outcome.”


The research group, headed by Jian Z. Wang, PhD, medical physicist and the director of the Radiation Response Modeling Program at the Ohio State University, followed 80 women with various stages of cervical cancer—with tumors ranging from the size of a cherry to the size of a grapefruit. All of the patients received MRI scans before, during and after radiation therapy—the standard treatment for cervical cancer. With these scans, the researchers could measure the change in tumor volume over the course of the cancer therapy.


The team developed a mathematical model to fit the tumor volume data from the MRI scans and, using this model, identified two factors that correlated with the likelihood of a patient’s cancer returning. The first is the patient’s radiation sensitivity—essentially, the percentage of the cells that survived the radiation dose. The higher this number, the worse the outcome. More specifically, if radiation killed 30 percent or more of a woman’s tumor cells during each day of treatment, then she is 33 percent more likely to be cancer-free than a woman whose tumor is more resistant to the radiation. The second factor is how quickly the dead cells are removed from the tumor area. For example, if it takes more than 22 days to clear the dead tumor cells after treatment, then that woman is nearly four times as likely to have her cancer return later on compared to a woman whose body clears the dead cells more quickly. When these two factors indicate that the cancer is likely to return, alternative treatments may be suggested for the patient. “Maybe we can use a more aggressive intervention instead,” Wang says.


This is a very active area of research, says William Small, Jr, MD, professor of radiation oncology at Northwestern University Medical School. This kind of modeling could potentially be applied to other types of cancers treated with radiation. “It is very important to try to identify outcomes with surrogate markers,” he says. “Doing so could allow us to finish clinical trials much quicker and dramatically improve our ability to test new therapies.”

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