Shang Jingjie, Tan Zhiqiang, Cheng Yong, Tang Yongjin, Guo Bin, Gong Jian, Ling Xueying, Wang Lu, Xu Hao
Department of Nuclear Medicine and PET/CT-MRI Center, The First Affiliated Hospital of Jinan University, No. 613 West Huangpu Road, Guangzhou, 510630, China.
EJNMMI Phys. 2021 Feb 8;8(1):12. doi: 10.1186/s40658-021-00358-7.
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 (FV) and whole-body fat mass (FM). 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 FV were significantly correlated with the FM (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 (SUL) 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 SUL close to the threshold.
标准化摄取值（SUV）经去脂体重（[LBM] SUL）归一化后，被PERCIST 1.0推荐作为衡量指标。詹姆斯预测方程（PE）是常用的估算LBM的公式，但对个体而言可能会导致较大误差。本研究的目的是引入一种通过PET/CT检查的有限覆盖（LC）CT图像估算LBM的新颖且可靠的方法，并测试其有效性，然后基于詹姆斯PE估算的LBM，分析经基于LC的LBM归一化的SUV是否会改变PERCIST 1.0反应分类。
首先，回顾性检索199例接受全身PET/CT检查的患者。基于LC脂肪体积（FV）与全身脂肪量（FM）之间的关系，建立了患者特异性的LBM方程。该方程在97例同样接受全身PET/CT检查的独立样本中进行交叉验证。将其结果与全身CT测量的LBM（参考标准）以及詹姆斯PE的结果进行比较。然后，回顾性检索241例实体瘤患者，这些患者在治疗前后均接受了PET/CT检查。根据基于PE和基于LC的PERCIST 1.0评估治疗反应。使用科恩κ系数和威尔科克森符号秩检验评估它们之间的一致性。评估不同LBM算法对PERCIST 1.0分类的影响。
FV与FM显著相关（r = 0.977）。此外，用LC图像评估的LBM测量结果比詹姆斯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）。这些不一致患者的峰值SUL（SUL）百分比变化均在高于或低于阈值（±30%）10%的区间内，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算法的变异性是一个值得关注的问题。经基于LC的LBM归一化的SUV可能会改变基于詹姆斯PE估算的LBM的PERCIST 1.0反应分类，特别是对于SUL百分比变化接近阈值的患者。
