Machine learning could speed up radiation therapy for cancer patients
2/7/2007 Hickory, NC staff www.hulliq.com A new computer-based technique could eliminate hours of manual adjustment associated with a popular cancer treatment. In a paper published in the Feb. 7 issue of Physics in Medicine and Biology, researchers from Rensselaer Polytechnic Institute and Memorial Sloan-Kettering Cancer Center describe an approach that has the potential to automatically determine acceptable radiation plans in a matter of minutes, without compromising the quality of treatment. "Intensity Modulated Radiation Therapy (IMRT) has exploded in popularity, but the technique can require hours of manual tuning to determine an effective radiation treatment for a given patient," said Richard Radke, assistant professor of electrical, computer, and systems engineering at Rensselaer. Radke is leading a team of engineers and medical physicists to develop a "machine learning" algorithm that could cut hours from the process. A subfield of artificial intelligence, machine learning is based on the development of algorithms that allow computers to learn relationships in large datasets from examples. Radke and his coworkers have tested their algorithm on 10 prostate cancer patients at Memorial Sloan-Kettering. They found that for 70 percent of the cases, the algorithm automatically determined an appropriate radiation therapy plan in about 10 minutes. "The main goal of radiation therapy is to irradiate a tumor with a very high dose, while avoiding all of the healthy organs," Radke said. He described early versions of radiation therapy as a "fire hose" approach, applying a uniform stream of particles to overwhelm cancer cells with radiation. IMRT adds nuance and [...]