Abstract

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.

Notes

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

Keywords

Prostate, IMRT

Subject Categories

Prostate--Cancer--Treatment

Disciplines

Engineering

Publisher

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

Publication Date

2007

Rights Holder

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



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