Using Knowledge-Based Models to Teach Complex Lung IMRT Planning

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Knowledge-based treatment planning models are commonly built from straightforward principles and utilize experience that it is able to learn from previous high-quality plans. This knowledge can be harnessed by having an e-learning system incorporating knowledge-based treatment planning models to serve as informative, efficient bases to train individuals to develop IMRT plans for a particular site while building confidence in utilizing these models in a clinical setting.

A previously developed beam angle selection model and a previously developed DVH prediction model for lung/mediastinum IMRT planning are used as the information centers within a directed e-learning system guided by scoring criteria and communicated with the trainees via a user interface ran from the treatment planning system (Eclipse). The scoring system serves both to illustrate relative quality of plans and to serve as a guide to facilitate directed changes within the plan. One patient serves as a benchmark to show skill development from the e-learning system and is completed without intervention. Five additional lung/mediastinum patients follow in the subsequent training pipeline where the models, graphical user interface (GUI) and trainer work with trainee’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 against the clinically delivered plan.

Trainees scored an average of 22.7% of the total points within the scoring criteria for their benchmark yet improved to an average of 51.9% compared to the clinically delivered plan which achieved 54.1% of the total potential points. Two of the five trainee final plans were rated as comparable to the clinically delivered by a physician and all five were noticeably improved by the physicians standards. For plans within the system, trainees performed on average 24.5% better than the clinically delivered plan with respect to the scoring criteria.

This first attempt at creating a dynamic interface communicating prior experience built in models to an end-user was approximately 10 hours to rapidly improve planning quality. It brings unexperienced planners to a level comparable of experienced dosimetrists for a specific treatment site and when used to inform decisions, the knowledge-based models aided in producing high quality plans.





Mistro, Matthew (2019). Using Knowledge-Based Models to Teach Complex Lung IMRT Planning. Master's thesis, Duke University. Retrieved from


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