Algorithm targets cancer

A new computer-based technique could eliminate hours of manual adjustment associated with a popular cancer treatment.


Intensity Modulated Radiation Therapy (IMRT) has exploded in popularity for treating cancer, but the technique can require hours of manual tuning to determine an effective radiation treatment for a given patient.


Now, a team led by Richard Radke, assistant professor of electrical, computer, and systems engineering at Rensselaer and his associates at the Memorial Sloan-Kettering Cancer Center in the US, are developing a means to perform the tuning operation automatically.


Radke and his co-workers have developed an algorithm to do just that and have tested it 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 tumour 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 flexibility to radiation therapy, increasing the likelihood of treating a tumour 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 tumour 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 tumour 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 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.


Radke and his co-workers plan to develop a more robust prototype that can be installed on hospital computers and evaluated in a clinical setting. He hopes to see a clinical prototype in place at Memorial Sloan-Kettering in the next few years.


The researchers also plan to test the approach on tumours that are more difficult to treat with radiation therapy, such as head and neck cancers.


In a related project, Radke is collaborating with colleagues at Boston’s Massachusetts General Hospital to create computer vision algorithms that offer accurate estimates of the locations of tumours. This automatic modelling and segmentation process could help radiation planning at an earlier stage by automatically outlining organs of interest in each image of a CT scan, which is another time-consuming manual step.