Purpose: To develop an e-learning system by incorporating knowledge-based treatment planning models to serve as informative, efficient bases to train individuals to develop optimal IMRT plans while creating confidence in utilizing these models in clinical settings.
Methods: A beam angle selection model and a DVH prediction model for lung/mediastinum IMRT planning are used as knowledge bases within a directed e-learning system guided by scoring criteria and communicated with the trainees via a user interface ran from Eclipse. The scoring serves both to illustrate relative quality of plans and to guide directed changes within the plan. One mediastinum case serves as a benchmark to show skill change from the e-learning system and is completed without intervention. Five additional lung/mediastinum cases follow in the subsequent training pipeline where the models, GUI and trainer work with traineeâ€™s directives and guide meaningful beam selection and tradeoffs within IMRT optimization. Five trainees with minimal treatment planning background were evaluated by both the scoring criteria and a physician to look for improved planning quality and relative effectiveness compared to the clinically delivered plan.
Results: Trainees scored an average of 22.7% of total points within the scoring criteria for their benchmark yet improved to an average of 51.9%; the clinically delivered plan achieved 54.1% of total potential points. Two of the five trainee final plans were rated as comparable to the clinically delivered by a physician; all five trainees rated as noticeably improved. For the five plans within the system, trainees scored, on average, 24.5% higher than the respective clinically delivered plan.
Conclusion: This first attempt at creating a dynamic interface communicating the prior experience inherent to these models to an end-user took approximately 10 hours and rapidly improved planning quality. It brings unexperienced planners to a level comparable of experienced dosimetrists for a specific treatment site.
Funding Support, Disclosures, and Conflict of Interest: This work was partially supported by NIH/NCI 1R01CA201212
Not Applicable / None Entered.