Teaching machines to learn? [insert joke about machines becoming self-aware and taking over the world] Being on the serious side, researchers at the Rensselaer Polytechnic Institute have developed a “machine learning” algorithm which can correctly determine appropriate radiation therapy in as little as 10 minutes.
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 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. They found that for 70 percent of the cases, the algorithm automatically determined an appropriate radiation therapy plan in about 10 minutes…
IMRT adds nuance and flexibility to radiation therapy, increasing the likelihood of treating a tumor without endangering surrounding healthy tissue. Each IMRT beam is composed of thousands of tiny “beamlets” that can be individually modulated to deliver the right level of radiation precisely where it is needed.
But the semi-automatic process of developing a treatment plan can be extremely time-consuming – up to about four hours for prostate cancer and up to an entire day for more complicated cancers in the head and neck, according to Radke.
A radiation planner must perform a CT scan, analyze the image to determine the exact locations of the tumor and healthy tissues, and define the radiation levels that each area should receive. Then the planner must give weight to various constraints set by a doctor, such as allowing no more than a certain level of radiation to hit a nearby organ, while assuring that the tumor receives enough to kill the cancerous cells.
This is currently achieved by manually determining the settings of up to 20 different parameters, or “knobs,” deriving the corresponding radiation plan, and then repeating the process if the plan does not meet the clinical constraints. “Our goal is to automate this knob-turning process, saving the planner’s time by removing decisions that don’t require their expert intuition,” said Radke.
The researchers first performed a sensitivity analysis, which showed that many of the parameters could be eliminated completely because they had little effect on the outcome of the treatment. They then showed that an automatic search over the smaller set of sensitive parameters could theoretically lead to clinically acceptable plans.
The procedure was put to the test by developing radiation plans for 10 patients with prostate cancer. In all 10 cases the process took between five and 10 minutes, Radke said. Four cases would have been immediately acceptable in the clinic; three needed only minor “tweaking” by an expert to achieve an acceptable radiation plan; and three would have demanded more attention from a radiation planner.