Raveendran Vysakh, R Ganapathi Raman, P T Anjana, Bhasi Saju, C P Ranjith, Kinhikar Rajesh Ashok
Department of Radiation Oncology, Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai, Maharashtra, India.; Department of Physics, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District, Tamil Nadu, India..
Department of Physics, Saveetha Engineering College (Autonomous), Chennai, Tamil Nadu, India.
Phys Med. 2023 Aug;112:102651. doi: 10.1016/j.ejmp.2023.102651. Epub 2023 Aug 8.
Monitoring Radiotherapy Quality Assurance (QA) using Statistical Process Control (SPC) methods has gained wide acceptance. The significance of understanding the SPC methodologies has increased among the medical physics community with the release of Task Group (TG) reports from the American Association of Physicists in Medicine (AAPM) on patient-specific QA (PSQA) (TG-218) and Proton therapy QA (TG-224). Even though these reports recommend using SPC for QA analysis, physicists have ambiguities and doubts in choosing proper SPC tools and methodologies. This review article summarises the utilisation of SPC methods for different Radiotherapy QAs published in the literature, such as PSQA, routine Linac QA and patient positional verification. QA analysis using SPC could assist the user in distinguishing between 'special' and 'routine' sources of variations in the QA, which can aid in reducing actions on false positive QA results. For improved PSQA monitoring, machine-specific, site-specific, and technique-specific Tolerance Limits and Action Limits derived from a two-stage SPC-based approach can be used. Adopting a combination of Shewhart's control charts and time-weighted control charts for routine Linac QA monitoring could add more insights to the QA process. Incorporating SPC tools into existing image review modules or introducing new SPC software packages specifically designed for clinical use can significantly enhance the image review process. Proper selection and having adequate knowledge of SPC tools are essential for efficient QA monitoring, which is a function of the type of QA data available, and the magnitude of process drift to be monitored.
使用统计过程控制（SPC）方法监测放射治疗质量保证（QA）已获得广泛认可。随着美国医学物理学家协会（AAPM）任务组（TG）发布关于患者特定质量保证（PSQA）（TG - 218）和质子治疗质量保证（TG - 224）的报告，医学物理界对理解SPC方法的重要性有所增加。尽管这些报告推荐使用SPC进行质量保证分析，但物理学家在选择合适的SPC工具和方法时仍存在模糊性和疑问。这篇综述文章总结了文献中发表的不同放射治疗质量保证中SPC方法的应用，如PSQA、直线加速器常规质量保证和患者位置验证。使用SPC进行质量保证分析可以帮助用户区分质量保证中“特殊”和“常规”的变异来源，这有助于减少对假阳性质量保证结果的处理。为了改进PSQA监测，可以使用基于两阶段SPC方法得出的特定机器、特定部位和特定技术的公差限和行动限。将休哈特控制图和时间加权控制图结合用于直线加速器常规质量保证监测，可以为质量保证过程提供更多见解。将SPC工具纳入现有的图像审查模块或引入专门为临床使用设计的新SPC软件包，可以显著增强图像审查过程。正确选择并充分了解SPC工具对于高效的质量保证监测至关重要，这取决于可用质量保证数据的类型以及要监测的过程漂移程度。
