Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, via Venezian 1, 20133, Milan, Italy.
Physics Department, Università degli Studi di Milano and Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Giovanni Celoria 16, 20133, Milan, Italy.
Med Phys. 2019 Jun;46(6):2541-2555. doi: 10.1002/mp.13538. Epub 2019 Apr 29.
To apply Failure Mode and Effects Analysis (FMEA) to optimize linac quality control (QC) protocol in order to ensure patient safety and treatment quality, taking maximum advantage of the available resources.
Each parameter tested by the QC was considered as a potential failure mode (FM). For each FM, likelihood of occurrence (O), severity of effect (S), and lack of detectability (D) were evaluated and corresponding Risk Priority Number (RPN) was calculated from the product of three indexes. The scores were assigned using two methods: (a) A survey submitted to the medical physicists; (b) A semi-quantitative analysis (SQA) performed through: simulation of FMs in the treatment planning system; studies reported in literature; results obtained by the QC data analysis. A weighted RPN for all FMs was calculated taking into account both the methods. For each linac, the tests were then sorted by their frequency and the RPN value.
A high variability was found in the scores of the survey, although in many it was reduced in RPN values, highlighting the more relevant tests as on beam output and imaging system. Integrating these results with those obtained by SQA, the RPN-based ranking of tests has been provided considering the specific use of the accelerator: for example, more accurate tests on dose modulation and multileaf collimator speed were required in linacs where intensity-modulated treatment is performed, while, more specific tests on couch and jaw position indicators were necessary where treatments with multiple isocenters and/or junctions between adjacent fields were often delivered.
Failure Mode and Effects Analysis is a useful tool to prioritize the linac QCs, taking into account the specific equipment and clinical practice. The integration of SQA and survey results reduces subjectivity of the FMEA scoring and allows to optimize linac QCs without "losing" the expertise and experience of medical physicists and clinical staff.
应用失效模式与效应分析（FMEA）优化直线加速器质量控制（QC）方案，以确保患者安全和治疗质量，同时充分利用现有资源。
QC 测试的每个参数均视为潜在失效模式（FM）。对每个 FM，评估发生的可能性（O）、影响的严重程度（S）和检测的不敏感性（D），并根据三个指标的乘积计算相应的风险优先数（RPN）。使用两种方法分配分数：（a）向医学物理学家提交调查；（b）通过以下方式进行半定量分析（SQA）：在治疗计划系统中模拟 FM；文献报道的研究；QC 数据分析的结果。通过两种方法，计算所有 FM 的加权 RPN。对于每台直线加速器，按其频率和 RPN 值对测试进行排序。
调查的评分存在很大差异，但在许多情况下 RPN 值降低，突出了与射束输出和成像系统更相关的测试。将这些结果与 SQA 获得的结果相结合，根据加速器的具体用途提供了基于 RPN 的测试排名：例如，在进行强度调制治疗的直线加速器中，需要对剂量调制和多叶准直器速度进行更精确的测试，而在经常进行多中心点和/或相邻野之间交界处治疗的情况下，需要对治疗床和牙弓位置指示器进行更具体的测试。
失效模式与效应分析是一种有用的工具，可用于根据特定设备和临床实践对直线加速器 QC 进行优先级排序。SQA 和调查结果的整合降低了 FMEA 评分的主观性，并允许在不“失去”医学物理学家和临床工作人员的专业知识和经验的情况下优化直线加速器 QC。
