Intensity-modulated radiotherapy (IMRT) remains the standard of care for external beam radiation therapy for head and neck cancers. Planning for IMRT requires a trial-and-error approach that is completely dependent on planner expertise and time available for multiple iterations of manual optimization adjustments. Knowledge-based radiation therapy planning utilizes a database of previously planned Duke University Medical Center patient plans to create clinically comparable treatment plans by comparing the geometrical two-dimensional projections of the planning target volume (PTV) and organs at risk (OAR). These 2D beam;;s eye view (BEV) images are first aligned with squared error registration, then the similarity is computed using the mutual information (MI) metric. After the closest match is found, computed constraints and deformed fluence maps are entered into Eclipse treatment planning system to generate the new knowledge-based treatment plan. For this study, 20 randomly selected cases were matched against a database of 103 head and neck cancer cases. The resulting new plans were compared to their clinically planned counterparts. For these 20 cases, 13 proved to be dosimetrically comparable by evaluation of the PTV dose-volume histogram. In 92% of cases planned, at least half of the OARs were also deemed comparable or better than the original plan. These cases were planned in less than 25 minutes with no manual constraint objective adjustments, as opposed to many hours needed in clinical planning.
