Multi‐institution single geometry plan complexity characteristics based on IROC phantoms

Abstract

AbstractBackgroundClinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution‐specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives.PurposeTo assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans.Methods1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC‐descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k‐means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin‐Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS.ResultsThe IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k‐means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans.ConclusionsThere is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients.