Training and evaluation of a knowledge-based model for automated treatment planning of multiple brain metastases

Abstract

Aim: Volumetric modulated arc therapy (VMAT) has been utilized to plan and treat multiple cranial metastases using a single isocenter due to its ability to provide steep dose gradients around targets as well as low doses to critical structures. VMAT treatment is delivered in a much shorter time compared to using a single isocenter for the treatment of each lesion. However, there is a need to develop methods to reduce the treatment planning time for these cases while also standardizing the plan quality. In this work we demonstrate the use of RapidPlan, which is knowledge-based treatment (KBP) planning software to plan multiple cranial SRS cases.

Methods: The 66 patient plans with 125 lesions (range 1-4, median 1) were used to train a model. In addition, the model was validated using 10 cases that were previously treated and chosen randomly. The clinical plans were compared to plans generated by RapidPlan for target coverage and critical organ dose.

Results: Coverage to the target volume, gradient index, conformity index and minimum dose to the target showed no significant difference between the original clinical plan vs. the plan generated by KBP. A comparison of doses to the critical organs namely the brainstem, brain, chiasm, eyes, optic nerves and lenses showed no significant difference. Target dose homogeneity was slightly better with the clinical plan, however this difference was also statistically insignificant.

Conclusion: This work demonstrates that KBP can be trained and efficiently utilized to help not only speed up the planning process but also help standardize the treatment plan quality.

Keywords

Brain metastases / radiotherapy / volumetric modulated arc therapy / knowledge-based planning / stereotactic / radiosurgery

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