To simplify the trial-and-error process of adjusting objective function parameters (e.g. weights, dose limits) in prostate IMRT planning, we present a feasibility study showing that machine learning followed by a sensitivity-driven greedy search can quickly and automatically determine parameters that lead to a plan meeting the clinical requirements. The training database is composed of a large number of plans treated effectively under the 8640cGy prostate IMRT protocol. For each plan, the output features include the clinical setting of parameters, and the input features include simple dose statistics resulting from several fixed settings of parameters. We predict a new plan based on the 3 nearest plans in the training database that have similar input features. Starting from such a pre-plan, a sensitivity based automatic parameter search is applied to improve the plan's deficiencies. Experiments on a 39-patient dataset showed that a clinically acceptable (based on simplified dose calculation) prostate IMRT plan could be automatically determined within 2 minutes of optimization.


Poster presented at the 2007 Thrust R2D Image Understanding and Sensor Fusion Methods Conference


Prostate, IMRT

Subject Categories





Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

Publication Date


Rights Holder

Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)

Click button above to open, or right-click to save.

Included in

Engineering Commons