Memorial Sloan-Kettering Cancer Center at Basking Ridge, NJ, United States of America.
Biomed Phys Eng Express. 2023 Aug 3;9(5). doi: 10.1088/2057-1976/ace6a1.
The performance of a linear accelerator (Linac) depends on the integrity of its x-ray target. The sudden failure of its target not only breaks down the Linac but also could contribute significant disruptions to patient care. This work is to develop a predicative quality assurance (QA) method using Statistical Process Control (SPC) and AutoRegressive Integrated Moving Average (ARIMA) modeling to identify the risk of target failure before it occurs. In the past years, we observed two incidents of target failure among our Linacs. Retrospectively, we collected past daily QA data (from both open fields and enhanced dynamic wedge (EDW) measurements) and analyzed its historical trend using methods of SPC and ARIMA. SPC is a technique that monitors process performance based on statistical analysis. ARIMA is a time-series forecasting algorithm that can be used to estimate future values based on its past pattern. Both have been evaluated for predictive QA in radiotherapy. Application of SPC on open beam QA data would not yield an early warning signal to the pending target failures. However, when the same SPC methodology applies to EDW measurements, the control limits were breached a couple of weeks before the target failed. EDW mechanism introduces nonuniform magnification factors over its wedge-directed beam profiles and is responsible for the sensitivity of its profile to changing beam properties induced by a degrading target. Further extension of the warning period may be possible by using ARIMA modeling. Predicative QA for EDW daily data using SPC and ARIMA methods may provide an early QA warning to incoming Linac target failure.
直线加速器（Linac）的性能取决于其 X 射线靶的完整性。靶的突然失效不仅会导致 Linac 崩溃，还可能对患者护理造成重大干扰。这项工作旨在开发一种使用统计过程控制（SPC）和自回归综合移动平均（ARIMA）建模的预测性质量保证（QA）方法，以在靶失效发生之前识别其失效风险。在过去几年中，我们观察到我们的 Linacs 中有两起靶失效事件。回顾性地，我们收集了过去的日常 QA 数据（来自开放野和增强动态楔形（EDW）测量），并使用 SPC 和 ARIMA 方法分析了其历史趋势。SPC 是一种基于统计分析监测过程性能的技术。ARIMA 是一种时间序列预测算法，可用于根据其过去的模式估计未来的值。两者都已在放射治疗的预测性 QA 中进行了评估。SPC 在开放束 QA 数据上的应用不会对即将发生的靶失效发出早期预警信号。然而，当相同的 SPC 方法应用于 EDW 测量时，控制限在靶失效前几周就被突破了。EDW 机制在其楔形定向光束轮廓上引入了不均匀的放大因子，并且对由于劣化靶引起的光束特性变化的轮廓敏感。通过使用 ARIMA 建模，可能会有更长的预警期。使用 SPC 和 ARIMA 方法对 EDW 日常数据进行预测性 QA 可能会为即将到来的 Linac 靶失效提供早期 QA 预警。
