Sensitivity of Three Patient-Specific Quality Assurance Systems to MLC Aperture Errors With Volumetric Modulated Arc Therapy

Abstract

Purpose: To compare the sensitivity of ArcCHECK (AC), portal dosimetry (PD), and an in-house logfile-based system (LF) to multileaf collimators (MLC) aperture errors and the ability to identify these errors. Methods and Materials: For 12 retrospective original head and neck volumetric modulated arc therapy (VMAT) plans, MLC aperture errors of ± 0.4mm, ± 1.2mm, ± 2mm, and ± 3mm were introduced for each plan, resulting in 96 plans with errors. AC, PD, and LF were used for the gamma evaluation at 3%/3mm, 3%/2mm, and 2%/2mm criteria. Gradient analysis was used to evaluate the sensitivity to MLC aperture errors. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve was used to evaluate the ability to identify MLC aperture errors and dose errors, and the optimal cut-off value to identify the error was obtained. Results: The gamma pass rate (%GP) of LF had the smallest descent gradient as the MLC error increases in any case. The descent gradient of PD was larger than AC, except for the case at the 2%/2mm criteria. For the 3%/3mm criteria, the MLC aperture errors that can be perfectly identified by AC, PD, and LF were ± 3mm, ± 2mm, and ± 1.2mm, respectively, and the average percent dose error (%DEs) of dose metrics in targets that can be perfectly identified were 4% to 5%, 3% to 4%, and 2% to 3%, respectively. For the 3%/2mm criteria, the errors that AC, PD, and LF can perfectly identify were the same as the 3%/3mm criteria. For the 2%/2mm criteria, AC can perfectly identify the MLC error of ± 2mm and the %DE of 3% to 4%. PD and LF can identify the MLC error of ± 1.2mm and the %DE of 2% to 3%. Conclusion: Different patient-specific quality assurance (PSQA) systems have different sensitivity and recognition abilities to MLC aperture errors. Institutions should formulate their own customized %GP limits based on their PSQA process through ROC or other methods.

Pylinac: Image analysis for routine quality assurance in radiotherapy

“…Pylinac has been used widely in literature since its release in 2014, either validation of the algorithms for use by individual clinics (Boudet et al, 2022;Bredikin & Walsh, 2022;Ji & Cong, 2022;Lay, Chuang, Wu, et al, 2022), as a research tool for other ends (Alexander et al, 2021;Al-Kabkabi et al, 2022;Bozhikov et al, 2019;Cullom et al, 2021;Hu et al, 2022;Huang et al, 2021;Mendes et al, 2022;Pant et al, 2020;Pearson et al, 2022;Salari et al, 2023;Tegtmeier et al, 2022;Wang et al, 2020;Wojtasik et al, 2020), or used within other packages (Chuang et al, 2021;Oliver et al, 2022) assurance of IMRT using UNet++: Classification, gamma passing rates prediction, and dose difference prediction. Frontiers in Oncology, 11. https://doi.org/10.3389/fonc.2021.700343…”