Complexity‐based unsupervised machine learning for patient‐specific VMAT quality assurance

This study demonstrated the potential of clustering analysis to unravel hidden patterns of plan complexity in dosimetric quality assurance of VMAT treatments. The results suggested that a three-clusters classification scheme has a true basis in plan complexity, supporting the hypothesis that the MCS metric strongly underlies PSQA results.