Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases.

Abstract

Dosimetric potential of knowledge-based RapidPlan planning model trained with HyperArc plans (Model-HA) for brain metastases has not been reported. We developed a Model-HA and compared its performance with that of clinical volumetric modulated arc therapy (VMAT) plans. From 67 clinical stereotactic radiosurgery (SRS) HyperArc plans for brain metastases, 47 plans were used to build and train a Model-HA. The other 20 clinical HyperArc plans were recalculated in RapidPlan system with Model-HA. The model performance was validated with the 20 plans by comparing dosimetric parameters for normal brain tissue between clinical plans and model-generated plans. The 20 clinical conventional VMAT-based SRS or stereotactic radiotherapy plans (CL-VMAT) were reoptimized with Model-HA (RP) and HyperArc system (HA), respectively. The dosimetric parameters were compared among three plans (CL-VMAT vs. RP vs. HA) in terms of planning target volume (PTV), normal brain excluding PTVs (Brain-PTV), brainstem, chiasm, and both optic nerves. In model validation, the optimization performance of Model-HA was comparable to that of HyperArc system. In comparison to CL-VMAT, there were no significant differences among three plans with respect to PTV coverage (p>0.17) and maximum dose for brainstem, chiasm, and optic nerves (p>0.40). RP provided significantly lower V20Gy , V12Gy , and V4Gy for Brain-PTV than CL-VMAT (p<0.01). The Model-HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.