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走向基于过程的放射治疗质量保证,使用统计过程控制

Vysakh Raveendran¹, Ganapathi Raman R², Anjana P T³

¹Department 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..

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

|August 10, 2023

概括

统计过程控制 (SPC) 通过区分常规变化和特殊原因来提高放射治疗质量保证 (QA). 这有助于减少假阳性,并改善患者特异性QA (PSQA) 和线性加速器 (Linac) 的QA监测.

科学领域:

医学物理医学物理
辐射疗法质量保证辐射疗法质量保证
统计过程控制统计过程控制

背景情况:

统计过程控制 (SPC) 方法越来越多地被推用于放射治疗质量保证 (QA),特别是针对患者的QA (PSQA) 和质子疗法QA,正如AAPM任务组 (TG) 报告所强调的那样.
医学物理学家在选择适当的SPC工具和方法来进行有效的质量保证分析时面临着挑战.
本综述整合了有关SPC应用在各种放射治疗QA领域的文献.

研究的目的:

总结统计过程控制 (SPC) 方法在不同放射治疗质量保证 (QA) 应用中的使用情况.
解决医学物理学家对SPC工具的选择和应用的质量保证的模两可和疑虑.
通过适当的SPC方法论,提供有关加强质量保证监测的见解.

主要方法:

在放射治疗QA中SPC应用的文献综述,包括患者特定QA (PSQA),常规线性加速器QA (Linac) 和患者位置验证.
分析SPC如何有助于区分质量保证数据的特殊和常规变异来源.
探索基于SPC的方法,为PSQA设定特定的机器,特定的地点和特定的技术的容忍度和行动限制.
检查控制图组合 (Shewhart's和时间加权) 用于常规的Linac QA.
讨论将SPC工具集成到现有的图像审查模块中或开发新的临床软件.

主要成果:

SPC分析有助于区分"特殊"和"常规"的变化,减少虚假阳性质量保证行动.
一种两阶段的SPC方法可以建立特定于机器,特定于地点和特定于技术的极限,以改善PSQA监测.
结合Shewhart和时间加权控制图表,可以为常规Linac QA提供更好的洞察力.
实施SPC工具可以显著改善放射治疗中的图像审查过程.

结论:

在放射治疗中,有效的质量保证监测依赖于适当选择和理解SPC工具,根据可用的数据和过程漂移量身定制.
SPC方法提供了一个强大的框架,以提高各种放射治疗质量保证程序的可靠性和效率.
采用SPC可以导致更准确的质量保证评估和优化治疗交付.

关键词:

利纳克公司QA 特定于患者的IMRT QA QA 放射治疗质量保证质量保证统计过程控制过程控制

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