Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases
Tomohiro Sagawa1, Yoshihiro Ueda1, Haruhi Tsuru2
1Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
Objective:
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.
Methods:
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.
Results:
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 V20 Gy , V12 Gy , and V4 Gy for Brain - PTV than CL-VMAT (p < 0.01).
Conclusion:
The Model-HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.
