Medical Physics Unit, Azienda USL Toscana Centro, Florence, Italy.
Department of Radiation Oncology, Hospital Clínic de Barcelona, Barcelona, Spain.
Med Phys. 2024 Jun;51(6):3961-3971. doi: 10.1002/mp.17081. Epub 2024 Apr 17.
Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolved challenge.
The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinical application.
A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the control process.
The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highly complex.
Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plan complexity.
统计过程控制（SPC）是一种用于过程监测的强大统计工具，已在医疗保健应用中得到高度推荐，包括放射治疗质量保证（QA）。AAPM TG-218 报告描述了 SPC 在容积调强弧形治疗（VMAT）治疗前验证中的临床实施，指出需要根据计划复杂性调整公差限。然而，计划复杂性的量化及其与 SPC 的集成仍然是一个未解决的挑战。
本研究的主要目的是研究将计划复杂性纳入 VMAT 治疗前验证的 SPC 框架中。该研究探讨并评估了将计划复杂性纳入 SPC 的各种策略，讨论了它们的优点和局限性，并为临床应用提供了建议。
使用 PTW OCTAVIUS 4D 设备对来自不同解剖部位的 309 个 VMAT 计划进行回顾性分析，进行 QA 测量。获得伽马通过率（GPR），并使用传统的 Shewhart 方法和三种启发式方法（缩放加权方差、加权标准偏差和偏度校正）计算下限控制，以适应非正态数据分布。使用“识别-消除-重新计算”方法进行稳健分析。分析了八个复杂性指标，并评估了将计划复杂性纳入 SPC 的两种不同策略。第一种策略侧重于为不同的治疗部位建立控制限，第二种策略则基于将控制限确定为单个计划复杂性的函数。该研究广泛研究了控制限与计划复杂性之间的相关性，并评估了复杂性指标对控制过程的影响。
使用 SPC 建立的控制限受治疗计划复杂性的强烈影响。在第一种策略中，发现控制限与每个部位的平均计划复杂性之间存在明显的相关性。第二种方法基于单个计划复杂性指标得出控制限，能够定制公差限。在这两种策略中，公差限与计划复杂性呈反比关系，导致所有高度复杂的计划都被归类为在控制之中。相比之下，当不考虑复杂性而对计划进行整体分析时，所有失控计划都非常复杂。
将计划复杂性纳入 VMAT 验证的 SPC 需要进行细致和全面的分析。为了确保整体过程控制，我们主张在治疗计划期间严格控制并最小化计划复杂性，特别是在根据计划复杂性调整控制限时。
