Standardized uptake value (SUV) normalized by lean body mass ([LBM] SUL) is recommended as metric by PERCIST 1.0. The James predictive equation (PE) is a frequently used formula for LBM estimation, but may cause substantial error for an individual. The purpose of this study was to introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James PE. First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FVLC) and whole-body fat mass (FMWB). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen’s κ coefficient and Wilcoxon’s signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated. The FVLC were significantly correlated with the FMWB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2%; κ = 0.823, P=0.837). These discordant patients’ percentage changes of peak SUL (SULpeak) were all in the interval above or below 10% from the threshold (±30%), accounting for 43.5% (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment. LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SULpeak close to the threshold.

中文翻译：

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一种基于有限覆盖CT图像的患者特定瘦体重评估方法及其在PERCIST中的应用：与预测方程的比较

PERCIST 1.0建议将以瘦体重（[LBM] SUL）标准化的标准化摄取值（SUV）作为指标。James预测方程（PE）是LBM估计的常用公式，但可能对个人造成重大误差。这项研究的目的是介绍一种新颖可靠的方法，通过PET / CT检查中的有限覆盖（LC）CT图像估算LBM，并测试其有效性，然后分析基于LC的LBM归一化的SUV是否可以改变PERCIST 1.0响应分类，基于James PE估计的LBM。首先，对199例接受了全身PET / CT检查的患者进行回顾性检索。根据LC脂肪量（FVLC）和全身脂肪量（FMWB）之间的关系，开发了针对患者的LBM方程。该方程式由97名患者的独立样本进行交叉验证，这些患者还接受了全身PET / CT检查。将其结果与来自全身CT（参考标准）的LBM的测量结果以及James PE的结果进行了比较。然后，回顾性分析了241例在治疗前后接受PET / CT检查的实体瘤患者。根据基于PE和基于LC的PERCIST 1.0评估治疗反应。使用Cohen的κ系数和Wilcoxon的符号秩检验来评估它们之间的一致性。评估了不同的LBM算法对PERCIST 1.0分类的影响。FVLC与FMWB显着相关（r = 0.977）。此外，LC图像评估的LBM测量结果比James PE获得的结果更接近参考标准。基于PE和基于LC的PERCIST 1.0分类在27例患者中不一致（11.2％；κ= 0.823，P = 0.837）。这些不一致的患者峰值SUL（SULpeak）的百分比变化都在阈值（±30％）的10％以上或以下，占该区域总患者的43.5％（27/62）。变异程度与治疗前后LBM的变化有关。PERCIST 1.0分类中依赖LBM算法的可变性是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0响应分类，特别是对于SULpeak百分比变化接近阈值的患者。基于PE和基于LC的PERCIST 1.0分类在27例患者中不一致（11.2％；κ= 0.823，P = 0.837）。这些不一致的患者峰值SUL（SULpeak）的百分比变化均在阈值（±30％）的10％以上或以下，占该区域总患者的43.5％（27/62）。变异程度与治疗前后LBM的变化有关。PERCIST 1.0分类中依赖LBM算法的可变性是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0响应分类，特别是对于SULpeak百分比变化接近阈值的患者。基于PE和基于LC的PERCIST 1.0分类在27例患者中不一致（11.2％；κ= 0.823，P = 0.837）。这些不一致的患者峰值SUL（SULpeak）的百分比变化均在阈值（±30％）的10％以上或以下，占该区域总患者的43.5％（27/62）。变异程度与治疗前后LBM的变化有关。PERCIST 1.0分类中依赖LBM算法的可变性是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0响应分类，特别是对于SULpeak百分比变化接近阈值的患者。这些不一致的患者峰值SUL（SULpeak）的百分比变化均在阈值（±30％）的10％以上或以下，占该区域总患者的43.5％（27/62）。变异程度与治疗前后LBM的变化有关。PERCIST 1.0分类中与LBM算法有关的可变性是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0响应分类，特别是对于SULpeak百分比变化接近阈值的患者。这些不一致的患者峰值SUL（SULpeak）的百分比变化均在阈值（±30％）的10％以上或以下，占该区域总患者的43.5％（27/62）。变异程度与治疗前后LBM的变化有关。PERCIST 1.0分类中依赖LBM算法的可变性是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0反应分类，特别是对于SULpeak百分比变化接近阈值的患者。0分类是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0响应分类，特别是对于SULpeak百分比变化接近阈值的患者。0分类是一个值得注意的问题。通过基于LC的LBM归一化的SUV可以根据James PE估计的LBM更改PERCIST 1.0响应分类，特别是对于SULpeak百分比变化接近阈值的患者。