BACKGROUND Quantitative measurement procedures need to be accurate and precise to justify their clinical use. Precision reflects deviation of groups of measurement from another, often expressed as proportions of agreement, standard errors of measurement, coefficients of variation, or the Bland-Altman plot. We suggest variance component analysis (VCA) to estimate the influence of errors due to single elements of a PET scan (scanner, time point, observer, etc.) to express the composite uncertainty of repeated measurements and obtain relevant repeatability coefficients (RCs) which have a unique relation to Bland-Altman plots. Here, we present this approach for assessment of intra- and inter-observer variation with PET/CT exemplified with data from two clinical studies. METHODS In study 1, 30 patients were scanned pre-operatively for the assessment of ovarian cancer, and their scans were assessed twice by the same observer to study intra-observer agreement. In study 2, 14 patients with glioma were scanned up to five times. Resulting 49 scans were assessed by three observers to examine inter-observer agreement. Outcome variables were SUVmax in study 1 and cerebral total hemispheric glycolysis (THG) in study 2. RESULTS In study 1, we found a RC of 2.46 equalling half the width of the Bland-Altman limits of agreement. In study 2, the RC for identical conditions (same scanner, patient, time point, and observer) was 2392; allowing for different scanners increased the RC to 2543. Inter-observer differences were negligible compared to differences owing to other factors; between observer 1 and 2: -10 (95 % CI: -352 to 332) and between observer 1 vs 3: 28 (95 % CI: -313 to 370). CONCLUSIONS VCA is an appealing approach for weighing different sources of variation against each other, summarised as RCs. The involved linear mixed effects models require carefully considered sample sizes to account for the challenge of sufficiently accurately estimating variance components.

中文翻译：

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如何使用方差分量分析评估观察者内部和观察者之间与定量 PET 的一致性：标准化提案。


背景技术定量测量程序需要准确且精确，以证明其临床应用的合理性。精度反映了测量组与另一组测量值的偏差，通常表示为一致性比例、测量标准误差、变异系数或 Bland-Altman 图。我们建议采用方差分量分析（VCA）来估计 PET 扫描的单个元素（扫描仪、时间点、观察者等）造成的误差影响，以表达重复测量的复合不确定性并获得相关的重复性系数（RC），与布兰德-奥特曼情节有独特的关系。在这里，我们提出了这种通过 PET/CT 评估观察者内和观察者间变异的方法，并以两项临床研究的数据为例。方法 在研究 1 中，对 30 名患者进行术前扫描以评估卵巢癌，并由同一观察者对他们的扫描进行两次评估，以研究观察者内部的一致性。在研究 2 中，14 名神经胶质瘤患者接受了多达五次扫描。三位观察者评估了 49 次扫描结果，以检查观察者间的一致性。研究 1 中的结果变量为 SUVmax，研究 2 中的结果变量为大脑半球总糖酵解 (THG)。 结果 在研究 1 中，我们发现 RC 为 2.46，等于 Bland-Altman 一致性极限宽度的一半。在研究 2 中，相同条件（相同扫描仪、患者、时间点和观察者）的 RC 为 2392；允许使用不同的扫描仪将 RC 增加到 2543。与其他因素造成的差异相比，观察者间的差异可以忽略不计；观察者 1 和 2 之间：-10（95% CI：-352 至 332），观察者 1 与 3 之间：28（95% CI：-313 至 370）。 结论 VCA 是一种颇具吸引力的方法，用于权衡不同的变异来源（总结为 RC）。所涉及的线性混合效应模型需要仔细考虑样本大小，以应对足够准确地估计方差分量的挑战。