Treatment plan complexity quantification for predicting gamma passing rates in patient‐specific quality assurance for stereotactic volumetric modulated arc therapy

Abstract

The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.