A workflow to select local tolerance limits by combining statistical process control and error curve model
Abstract
Background
Patient‐specific quality assurance (QA) is a complicated process specific to personnel, equipment, and procedure. The universal or commonly used tolerance limits may not be applicable to local situations. Therefore, it is a need for a medical physicist to establish appropriate local tolerance limits based on actual situations and quantitatively evaluate the error sensitivity of selected tolerance limits to determine their availability in clinical practice.
Purpose
This study aims to develop a comprehensive and scientifically sound methodology for determining appropriate local tolerance limits in patient‐specific QA.
Methods and materials
A total of 214 RapidArc plans for cervical cancer were selected. Systematic multi‐leaf collimator (MLC) positional errors were simulated across eighteen offsets ranging from ± 0.2 to ± 5 mm. Dose verification was conducted on 808 RapidArc plans, and a retrospective review was carried out. Firstly, six commonly used QA metrics in gamma and DVH analysis were extracted from the QA results of 196 error‐free RapidArc plans. These QA metrics included GP 10 (gamma passing rates [GPRs] at 3%/2mm, 10% dose threshold), GP 50 (GPRs at 3%/2mm, 50% dose threshold), µGI 50 (mean gamma index at 3%/2mm, 50% dose threshold), PTV 95 (dose received by 95% of PTV), PTV 5 (dose received by 5% of PTV) and PTV mean (mean dose received by PTV). Secondly, the statistical process control was used to establish the corresponding tolerance limits for each metric. Then, six error curve models were created based on 360 error‐introduced plans to record changes in QA metrics under different magnitudes of MLC positional error. The error range of theoretical detection limits for systematic MLC positional errors was investigated to assess error sensitivity quantitatively using the error curve model. Finally, the process‐based tolerance limits of six single QA metrics and four combined QA metrics were validated by using 252 sets of test data. The binary classification performance (error‐free/error‐introduced) was assessed based on detection rate, accuracy, precision, recall, and f1‐score.
Results
The theoretical detection limits for process‐based tolerance limits of GP 10 , GP 50 , µGI 50 , PTV 95 , PTV mean , and PTV 5 were 2.19 mm, 2.71 mm, 3.52 mm, 1.93 mm, 3.20 mm, and 2.15 mm, respectively. In the validation phase, the process‐based tolerance limits for PTV 95 effectively identified systematic MLC positional errors exceeding 0.6 mm with a detection rate of 76.19%, displaying superior performance in binary classification among six single metrics. Regarding combined metrics, the joint evaluation of process‐based tolerance limits for GP 10 and PTV 95 showed a higher detection rate of 80.16% for systematic MLC positional errors exceeding 0.6 mm.
Conclusion
The proposed workflow integrates the establishment and validation of tolerance limits. It not only provides a practical tool for setting local tolerance limits based on actual clinical scenarios but also offers a quantitative method for medical physicists to understand the error sensitivity of the selected local tolerance limits.