Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China.
Med Phys. 2022 Feb;49(2):1312-1330. doi: 10.1002/mp.15346. Epub 2021 Nov 29.
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.
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.
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.
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.
使用正常的统计过程控制（SPC）方法，基于伽马通过率（GPR）建立患者特定质量保证（PSQA）过程的公差限会涉及到一些问题。本研究旨在：（a）展示启发式 SPC 方法可以替代分位数法来建立 PSQA 过程的公差限，且更稳健；（b）引入迭代的“识别-消除-重新计算”过程，用于根据未知状态下的回顾性 GPR 建立 PSQA 过程的公差限；（c）推荐一种根据实际临床回顾性 GPR 定义公差限的工作流程。
对四台直线加速器（linac）上的 1671 个容积调强弧形治疗（VMAT）预处理计划进行测量，并根据治疗部位使用 2%/2mm、3%/2mm 和 3%/3mm 标准下的 GPR 进行分析。使用 Anderson-Darling（AD）统计量进行正态性检验，使用 Fitter Python 包确定 GPR 的最优分布。使用迭代的“识别-消除-重新计算”过程来识别 PSQA 异常值。使用常规的 Shewhart 方法、两种转换方法、三种启发式方法和两种分位数方法，分别计算初始 PSQA、消除后的剩余 PSQA 和校正后的控制 PSQA 的公差限。综合比较和分析了不同方法、不同直线加速器和不同治疗部位下 PSQA 过程的公差限。
发现 75%的初始 PSQA 过程和 63%的控制过程不符合正态分布（AD 检验，p<0.05）。初始和控制 PSQA 的 GPR 最优分布随不同的直线加速器和治疗部位而变化。在实施“识别-消除-重新计算”过程中，由于异常值的影响，分位数方法无法有效地识别出失控 PSQA。使用最优拟合分布的分位数计算的控制 PSQA 的公差限代表了真实情况。使用启发式方法计算的控制 PSQA 公差限和消除后的剩余 PSQA 公差限非常接近真实情况（最大平均绝对偏差分别为 0.50%和 1.03%）。两种转换方法都存在转换失败的情况。对于 3%/2mm 伽马标准下的控制 PSQA，四个直线加速器和不同治疗部位的公差限最大差异分别为 3.10%和 5.02%。
PSQA 过程的 GPR 分布随不同的直线加速器和治疗部位而变化，但大多数呈偏态分布。在将 SPC 方法应用于 PSQA 过程时，启发式方法具有稳健性。对于控制 PSQA 过程，启发式方法计算的公差限与真实情况吻合较好。对于未知 PSQA 过程，经过迭代的“识别-消除-重新计算”过程后，启发式方法计算的公差限最接近真实情况。为 PSQA 过程设置特定于直线加速器和治疗部位的公差限对于临床应用是必要的。
