Diffusion MRI gives early indication if brain cancer therapy is effective

A special type of MRI scan that measures the flow of water molecules through the brain can help doctors determine early in the course of brain cancer regimen if a patient's tumor will shrink, a new study shows.

Researchers at the University of Michigan Comprehensive Cancer Center developed the assessment, which they call a functional diffusion map. They used a magnetic resonance imaging scan that tracks the diffusion, or movement, of water through the brain and mapped the changes in diffusion from the start of therapy to three weeks later. The tumor cells block the flow of water, so as those cells die, water diffusion changes.

In the study of 20 people with malignant brain tumors, the researchers found that any change in the functional diffusion map predicted 10 weeks before traditional techniques if the tumor was responding to the chemotherapy or radiation therapy. This has potential to spare patients from weeks of a grueling treatment regimen that's not working and gives doctors the opportunity to switch patients early on to a therapy that may be more effective.

Results of the study appear the week of March 28 in the early online edition of the Proceedings of the National Academy of Sciences.

Most primary brain tumors have a high mortality rate, with people surviving only 10 months after diagnosis. Typically, patients receive seven weeks of treatment, followed by a traditional MRI scan six weeks after completing therapy to determine if the tumor shrank. If the cancer did not respond to the treatment, a new approach may be tried.

Using diffusion MRI and the functional diffusion map, the U-M researchers were able to predict with 100 percent accuracy after only three weeks of treatment whether the therapy would be effective – 10 weeks before traditional methods would show a response.

"This is an important issue in terms of patient quality of life. Do you want to go through seven weeks of treatment only to find two months later that it had no effect? Using MRI tumor diffusion values to accurately predict the treatment response early on could allow some patients to switch to a more beneficial therapy and avoid the side effects of a prolonged and ineffective treatment," said Brian Ross, Ph.D., professor of radiology and biological chemistry at the U-M Medical School and a member of the U-M Comprehensive Cancer Center.

In the study, 20 participants with brain tumors underwent diffusion MRI before beginning a new treatment involving chemotherapy, radiation therapy or a combination. Three weeks later, they had another diffusion MRI. After finishing their treatment, the participants underwent standard MRI to determine whether their tumor responded to the therapy.

After three weeks – more than two months before the final MRI scan – researchers found significant differences between the patients' scans. Some areas reflected an increase in water diffusion, suggesting tumor cell death; other areas saw a decrease in diffusion, which Ross said could be accounted for by the swelling some cells undergo before dying; and in some participants, researchers saw no change in diffusion.

"In the end, we found if the diffusion changes in any way, up or down, it correlates to a positive outcome. The magnitude or amount of change relates to the effectiveness of treatment. This indicates a different mixture of cell death pathways within the tumors. In the end, any change is good. When you think about it, if the treatment is not having an effect, the tumor will continue to grow without any change to water diffusion," Ross said.

The researchers found that for each of the 20 patients, a change in the diffusion MRI accurately predicted the tumor's response. Researchers plan to test the technique with breast cancer and head and neck cancer.

In addition to Ross, U-M study authors are Bradford Moffat, Ph.D., assistant professor of radiology; Thomas Chenevert, Ph.D., professor of radiology; Theodore Lawrence, M.D., Ph.D., Isadore Lampe Professor and Chair of Radiation Oncology; Charles Meyer, Ph.D., professor of radiology; Timothy Johnson, Ph.D., adjunct assistant professor and assistant research scientist in biostatistics; Qian Dong, M.D., a radiology fellow; Christina Tsien, M.D., lecturer in radiation oncology; Suresh Mukherji, M.D., associate professor of radiology; Douglas Quint, M.D., professor of radiology; Stephen Gebarski, M.D., professor of radiology; Patricia Robertson, M.D., associate professor of neurology and of pediatrics and communicable diseases; Larry Junck, M.D., professor of neurology; and Alnawaz Rehemtulla, Ph.D., associate professor of environmental health sciences, radiation oncology and radiology.