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.

Radke and his coworkers 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 the next few years. The researchers also plan to test the approach on tumors 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 tumors. This automatic modeling 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. Learn more about this project here: http://news.rpi.edu/update.do?artcenterkey=134.

The research is supported by the National Cancer Institute and the Center for Subsurface Sensing and Imaging Systems (CenSSIS) at Rensselaer, which is funded by the National Science Foundation. Renzhi Lu, a graduate student in electrical engineering at Rensselaer, also contributed to the research.


Full caption: The automatic radiation planning algorithm results in beamlet intensities that produce equal-dose contours. The prostate (center) receives a high dose, while nearby tissue receives a low dose. Image by Rensselaer/Richard Radke

Back to HCB News

More Industry Headlines