Comparison of complexity metrics for multi-institutional evaluations of treatment plans in radiotherapy

Identifier:  imarina:9334295

Handle: https://hdl.handle.net/20.500.11797/imarina9334295

Authors:  Hernandez, V; Saez, J; Pasler, M; Jurado-Bruggeman, D; Jornet, N

Abstract:
Background and purpose: It is known that intensity-modulated radiotherapy plans that are highly complex might be less accurate in dose calculation and treatment delivery. Multiple complexity metrics have been proposed, but the relationships between them have not been thoroughly investigated. This study investigated these relationships in multi-institutional comparisons of treatment plans, where plans from multiple treatment planning systems (TPSs) are typically evaluated.Materials and methods: A program was developed to compute several complexity indices and provide analysis of dynamic plan parameters. This in-house software was used to analyse plans from a recent multi-institutional audit. Additionally, 100 clinical volumetric modulated arc therapy (VMAT) plans from two institutions using different TPSs were analysed.Results: All plans produced satisfactory pre-treatment verification results and, hence, complexity metrics could not be used to predict plans failing QA. Regarding the relationship among complexity indices, some very strong correlations were found (r > 0.9 with p < 0.01). However, some relevant discrepancies between complexity indices were obtained, even with negative correlation coefficients (r similar to-0.6) which were expected to be positive. These discrepancies could be explained because each complexity index focused on different features of the plan and different TPSs prioritised modulation of different plan parameters.Conclusions: Some complexity indices provided similar information and can be considered equivalent. However, indices that focused on different plan parameters yielded different results and it was unclear which complexity index should be used. Careful consideration should be given to the use of complexity metrics in multi-institutional studies.