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Stereotactic Radiosurgery for Gynecologic Cancer
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Dosimetric and planning efficiency comparison for lung SBRT: CyberKnife vs VMAT vs knowledge-based VMAT
Suhong Yu1, Huijun Xu2, Asa Sinclair1
1Department of Radiation Oncology, Boston Medical Center, Boston University school of medicine, Boston MA, USA.
Summary
This study compared CyberKnife (CK) and Varian Eclipse treatment planning systems for lung stereotactic body radiation therapy (SBRT). Eclipse-based VMAT achieved comparable plan quality to CK more efficiently, with knowledge-based VMAT offering better organ at risk sparing.
Area of Science:
- Radiation Oncology
- Medical Physics
- Cancer Treatment Technology
Background:
- Lung stereotactic body radiation therapy (SBRT) requires precise treatment planning.
- Comparing different treatment planning systems (TPS) is crucial for optimizing SBRT delivery.
- CyberKnife (CK) and Varian Eclipse are widely used TPS for SBRT.
Purpose of the Study:
- To compare treatment plan quality and planning efficiency for lung SBRT.
- Evaluate CyberKnife (CK) Multiplan versus Varian Eclipse systems (VMAT and KBP-VMAT).
- Assess dosimetric metrics and planning time across different techniques.
Main Methods:
- Retrospective analysis of 13 lung SBRT patients.
- Comparison of CK plans with Eclipse-generated VMAT and knowledge-based VMAT (KBP-VMAT) plans.
- Evaluation of metrics including monitor units, conformity indices, dose heterogeneity, spillage, OAR doses, and planning time.
Main Results:
- All techniques met clinical goals; CK plans used more monitor units (MU) and had less dose homogeneity.
- KBP-VMAT demonstrated superior organ at risk (OAR) sparing compared to VMAT.
- KBP-VMAT offered the shortest planning time, while VMAT achieved comparable plan quality to CK more efficiently.
Conclusions:
- Eclipse-based VMAT provides an efficient alternative to CK for lung SBRT with comparable plan quality.
- KBP-VMAT enhances OAR sparing but may increase MU requirements.
- Further optimization of KBP-VMAT is possible with site-specific models.

