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

Area of Science:

• Radiation Oncology
• Medical Physics

Background:

• Knowledge-based planning (KBP) models offer potential for optimizing radiation therapy.
• The dosimetric benefits of KBP models trained with HyperArc plans for brain metastases are not well-established.

Purpose of the Study:

• To develop and evaluate a KBP model (Model-HA) trained with HyperArc plans for brain metastases.
• To compare the dosimetric performance of Model-HA against clinical volumetric modulated arc therapy (VMAT) plans.

Main Methods:

• A Model-HA was developed using 47 clinical stereotactic radiosurgery (SRS) HyperArc plans.
• 20 clinical HyperArc plans were recalculated using Model-HA for validation.
• Clinical VMAT plans (CL-VMAT) were reoptimized with Model-HA (RP) and the HyperArc system (HA) for comparison.

Main Results:

• Model-HA validation showed comparable optimization performance to the HyperArc system.
• No significant differences were observed in PTV coverage or maximum doses to critical structures (brainstem, chiasm, optic nerves) among CL-VMAT, RP, and HA plans.
• RP significantly reduced normal brain dose (V20Gy, V12Gy, V4Gy) compared to CL-VMAT.

Conclusions:

• The developed Model-HA demonstrates significant potential for reducing normal brain radiation dose in brain metastases treatment.
• Model-HA offers an effective approach to improve upon existing VMAT plans for brain metastases SRS.