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Purpose: Establishing the tolerance limits of patient‐specific quality assurance (PSQA) processes based on the gamma passing rate (GPR) by using normal statistical process control (SPC) methods involves certain problems. The aim of this study was threefold: (a) to show that the heuristic SPC method can replace the quantile method for establishing tolerance limits in PSQA processes and is more robust, (b) to introduce an iterative procedure of "Identify‐Eliminate‐Recalculate" for establishing the tolerance limits in PSQA processes with unknown states based on retrospective GPRs, and (c) to recommend a workflow to define tolerance limits based on actual clinical retrospective GPRs. Materials and Methods: A total of 1671 volumetric‐modulated arc therapy (VMAT) pretreatment plans were measured on four linear accelerators (linacs) and analyzed by treatment sites using the GPRs under the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Normality testing was performed using the Anderson‐Darling (AD) statistic and the optimal distributions of GPRs were determined using the Fitter Python package. The iterative "Identify‐Eliminate‐Recalculate" procedure was used to identify the PSQA outliers. The tolerance limits of the initial PSQAs, remaining PSQAs after elimination, and in‐control PSQAs after correction were calculated using the conventional Shewhart method, two transformation methods, three heuristic methods, and two quantile methods. The tolerance limits of PSQA processes with different states for the respective methods, linacs, and treatment sites were comprehensively compared and analyzed. Results: It was found that 75% of the initial PSQA processes and 63% of the in‐control processes were non‐normal (AD test, p < 0.05). The optimal distributions of GPRs for the initial and in‐control PSQAs varied with different linacs and treatment sites. In the implementation of the "Identify‐Eliminate‐Recalculate" procedure, the quantile methods could not identify the out‐of‐control PSQAs effectively due to the influence of outliers. The tolerance limits of the in‐control PSQAs, calculated using the quantile of optimal fitting distributions, represented the ground truth. The tolerance limits of the in‐control PSQAs and remaining PSQAs after elimination calculated using the heuristic methods were considerably close to the ground truth (the maximum average absolute deviations were 0.50 and 1.03%, respectively). Some transformation failures occurred under both transformation methods. For the in‐control PSQAs at 3%/2 mm gamma criteria, the maximum differences in the tolerance limits for four linacs and different treatment sites were 3.10 and 5.02%, respectively. Conclusions: The GPR distributions of PSQA processes vary with different linacs and treatment sites but most are skewed. In applying SPC methodologies to PSQA processes, heuristic methods are robust. For in‐control PSQA processes, the tolerance limits calculated by heuristic methods are in good agreement with the ground truth. For unknown PSQA processes, the tolerance limits calculated by the heuristic methods after the iterative "Identify‐Eliminate‐Recalculate" procedure are closest to the ground truth. Setting linac‐ and treatment site‐specific tolerance limits for PSQA processes is necessary for clinical applications. [ABSTRACT FROM AUTHOR]

Purpose: Applying statistical process control (SPC) to intensity‐modulated radiotherapy (IMRT)/volumetric modulated arc therapy (VMAT) patient‐specific quality assurance (PSQA) program was recommended by the American Association of Physics in Medicine Task Group 218 report, but a comprehensive analysis of PSQA processes with non‐normal distributions is lacking. This study investigates SPC and process capability analysis (PCA) methods for non‐normal IMRT/VMAT PSQA processes. Methods: 1119 VMAT PSQAs were performed on three beam‐matched linear accelerators (linacs), using gamma analysis. The Anderson–Darling statistic was used to test normality. The control charts for each PSQA process were obtained using three non‐normal‐based methods and compared with the conventional Shewhart method. The ability of each PSQA process to produce an output within the specification limit was measured using the Cpk index; in this study, the Cpk index was calculated using two transformation methods and compared with that calculated using the conventional method. The performances of the three linacs were assessed using SPC and PCA methods. Results: All three PSQA processes were non‐normal (P < 0.005). Compared to the non‐normal‐based SPC and PCA methods, the false alarm rates of the conventional method for linac1, linac2, and linac3 were 0.83%, 3.77%, and 4.95% respectively; the minimum overestimated Cpk values were 0.59, 0.87, and 1.49, respectively. The process capabilities of the three beam‐matched linacs were at different levels. Conclusion: For non‐normal VMAT PSQA processes, the conventional SPC and PCA methods increase the false alarm rates and overestimate process capabilities. Instead, non‐normal‐based SPC and PCA methods are more reliable and accurate in non‐normal PSQA processes. Statistical process control and PCA are useful tools for assessing the performance of beam‐matched linacs. [ABSTRACT FROM AUTHOR]

This study describes a new plan complexity metric for volumetric-modulated arc therapy (VMAT) and evaluates the relationship of this metric with the VMAT dosimetric accuracy. The new modulation complexity score for VMAT (NMCSv) that is based on the aperture shape and multi-leaf collimator (MLC) leaf travel is described. Its performance is evaluated through correlation and receiver operating characteristic (ROC) analyses with patient-specific gamma passing rates using 2 3-dimensional diode arrays. For comparison, the following metrics are evaluated using the same correlation analyses: average field width, average leaf travel, modulation complexity score, and leaf travel modulation complexity score. Spearman's rank correlation analysis is performed to examine any relationships between the complexity metrics and the patient-specific gamma passing rates. ROC curves are used to assess the performance of the plan metrics using a gamma passing rate of 3%/3 mm criterion with a 95% tolerance level. In both the diode arrays, the gamma passing rates (3%/3 mm and 2%/2 mm) for patient-specific dosimetric verification of VMAT plans are moderately or weakly correlated to all the complexity metrics. NMCSv demonstrates the highest correlation with the passing rates (r = 0.652, p < 0.001 for Delta4 and r = 0.499, p < 0.001 for ArcCheck) and the highest area under the curve value (0.809, p < 0.01 for Delta4 and 0.734, p < 0.01 for ArcCheck). While using the Delta4 system, NMCSv exhibits an excellent classification performance with area under the curves of 0.926 (sensitivity: 0.913; specificity: 0.860; p < 0.01) and 0.918 (sensitivity: 0.943; specificity: 0.720; p < 0.01) for rectal and cervical cancer plans, respectively. NMCSv as a novel potential clinical plan complexity metric is moderately correlated with the gamma passing rate. It demonstrates the best performance with respect to distinguishing the dosimetric accuracy of VMAT plans among the evaluated metrics. The classification performance of complexity metrics can be affected by various dosimetry verification devices and treatment sites. [ABSTRACT FROM AUTHOR]