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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

Background: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; however, the ability of geometric indices to identify the error of contouring results is limited, mainly because it does not consider any clinical background. To study the relationship between geometric indices and dosimetric indices, four different types of targets were selected to introduce the systematic and random geometric errors in the delineation process. Materials and Methods: The C-shaped target outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) and the targets of three actual cases of oropharyngeal cancer, metastatic spine cancer, and prostate cancer were selected as the test contours that needed to be modified. Python software was used to perform translation, scaling, rotating and sine function transformation to introduce systematic and random errors into the above contours. These geometrically transformed contours were regarded as reference contours corrected by systematic and random errors. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric and dosimetric indices were quantified through linear regression. The Wilcoxon signed rank test was used to compare the ability of spatial-direction discrimination between the geometric indices of different directions of transformations. Results: The correlations between the geometric and dosimetric indices were inconsistent for the targets. For systematic errors, except for the sine function transformation (R²: 0.023–0.04, p＞0.05), the geometric transformations of the C-shaped target’s planning target volume (C-PTV) had correlations with the dosimetric indices D98% and Dmean (R²: 0.689–0.988), 80% of which were strongly correlated (R² > 0.8). For the random errors, the correlation coefficients of the actual cases were also high, R2 > 0.384, p < 0.05. The results of Wilcoxon signed rank test showed that only the p-values of volumetric geometric indices of C-PTV were less than 0.05. Conclusions: Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices. Keywords: Contour evaluation, Geometric indices, Dosimetric indices, Geometric transformation

Background : In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; However, the ability of geometric indices to identify the error of contouring results is limited, and there is lack of clinical background. Based on the reference contouring, we systematically introduced geometric errors to study the relationship between geometric and dosimetric indices and evaluated the clinical feasibility of assessing the accuracy of contouring based on geometric indices alone. Materials and Methods: A C-shaped target, organ at risk (Core), and intensity-modulated radiotherapy (IMRT) plan outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) were used as references. Translation, scaling, rotation (except for the Core), and sine function transformation were performed to simulate the contouring results. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric indices and dosimetric indices were quantified through linear regression. The clinical applicability of the threshold for geometric indices was analyzed by combining the geometric indices and dose difference diagram. Results: The correlations between the geometric and dosimetric indices were different and inconsistent for the contouring of the target and Core after the geometric transformation simulation. Except for the sine function transformation (R²: 0.04–0.023, p > 0.05), the other three geometric indices of the planning target volume (PTV) had strong correlations with the dosimetric indices D98% and D mean (R²: 0.689–0.988), 80% of which were strongly correlated with a p < 0.001. The correlation results for the other geometric transformations in the Core were similar to those in the PTV except for the down shift transformation. Conclusions: The dosimetric indices are heavily influenced by the contour differences, thus highlighting their importance in the evaluation process. Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices. Keywords: Contour evaluation, Geometric indices, Dosimetric indices, Geometric transformation simulation