An algorithm for computing radiation therapy treatments developed by researchers at North Carolina State University could substantially reduce patient side effects while delivering the same results as conventional radiation therapy.
That’s the finding of a new proof-of-concept study, funded by the National Science Foundation, published last week in Physics in Medicine and Biology involving model slices of five different liver tumors.
For decades, cancer patients who receive radiation therapy to destroy their tumors have been given a total dose split into multiple equal treatments. However, using a “spatiotemporal fractionation” approach, researchers contend that treatments delivered in different dose distributions in different fractions can be just as effective, while minimizing side effects.
“Conventional radiation treatments don’t necessarily achieve maximum benefit,” says Dávid Papp, assistant professor of mathematics at NC State University. “Our protocol, by delivering a high single-fraction dose to parts of the tumor during each fraction and a consistent lower dose to the liver and other healthy tissue, could reduce patient side effects substantially while maintaining the same effectiveness as conventional treatments.”
Reducing the radiation dose to healthy tissue while maintaining efficacy against the tumor is critical, according to Papp, whose model reduced the liver dose by 13 to 35 percent without compromising other clinical goals.
“We wanted to see what the quantitative benefits of such a new protocol would be,” adds Papp. “How much can you reduce the radiation’s effect on the liver while making sure that the tumor receives a consistent and effective dose? A reduction of 20 percent would reduce side effects enough to warrant a change in everyday clinical practice.”
He and his colleagues are continuing to refine the algorithm, with the hope of ultimately conducting in vivo testing.
“If it works for a particular tumor type, then maybe we can expand from there and see where else it could work,” Papp concludes.
Original Date: Jan 12 2018
Original Author: Greg Slabodkin