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基于CT的快速标准化方法确定PET的去脂体重SUV——相对于预测方程有显著改进

Rapid Standardized CT-Based Method to Determine Lean Body Mass SUV for PET-A Significant Improvement Over Prediction Equations.

作者信息

Riauka Terence A, Baracos Vickie E, Reif Rebecca, Juengling Freimut D, Robinson Don M, Wieler Marguerite, McEwan Alexander J B

机构信息

Division of Medical Physics, Department of Oncology, University of Alberta, Edmonton, AB, Canada.

Division of Palliative Care Medicine, Department of Oncology, University of Alberta, Edmonton, AB, Switzerland.

出版信息

Front Oncol. 2022 Jul 7;12:812777. doi: 10.3389/fonc.2022.812777. eCollection 2022.

DOI:10.3389/fonc.2022.812777
PMID:35875083
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9302197/
Abstract

UNLABELLED

In F-fluorodeoxyglucose (F-FDG) positron emission tomography (PET) studies, maximum standardized uptake value (SUV) is the parameter commonly used to provide a measurement of the metabolic activity of a tumor. SUV normalized by body mass is affected by the proportions of body fat and lean tissue, which present high variability in patients with cancer. SUV corrected by lean body mass (LBM), denoted as SUL, is recommended to provide more accurate, consistent, and reproducible SUV results; however, LBM is frequently estimated rather than measured. Given the increasing importance of a quantitative PET parameter, especially when comparing PET studies over time to evaluate disease response clinically, and its use in oncological clinical trials, we set out to evaluate the commonly used equations originally derived by James (1976) and Janmahasatian et al. (2005) against computerized tomography (CT)-derived measures of LBM.

METHODS

Whole-body F-FDG PET images of 195 adult patients with cancer were analyzed retrospectively. Representative liver SUV was normalized by total body mass. SUL was calculated using a quantitative determination of LBM based on the CT component of the PET/CT study (LBM) and compared against the equation-estimated SUL. Bland and Altman plots were generated for SUV-SUL differences.

RESULTS

This consecutive sample of patients undergoing usual care (men, n = 96; women, n = 99) varied in body mass (38-127 kg) and in Body Mass Index (BMI) (14.7-47.2 kg/m2). LBM weakly correlated with body mass (men, r = 0.32; women, r = 0.22), and thus SUV and SUL were also weakly correlated (men, r = 0.24; women, r = 0.11). Equations proved inadequate for the assessment of LBM. LBM estimated by James' equation showed a mean bias (overestimation of LBM compared with LBM) in men (+6.13 kg; 95% CI 4.61-7.65) and in women (+6.32 kg; 95% CI 5.26-7.39). Janmahasatian's equation provided similarly poor performance.

CONCLUSIONS

CT-based LBM determinations incorporate the patient's current body composition at the time of a PET/CT study, and the information garnered can provide care teams with information with which to more accurately determine FDG uptake values, allowing comparability over multiple scans and treatment courses and will provide a robust basis for the use of PET Response Criteria in Solid Tumors (PERCIST) in clinical trials.

摘要

未标注

在氟脱氧葡萄糖(F-FDG)正电子发射断层扫描(PET)研究中,最大标准化摄取值(SUV)是常用于衡量肿瘤代谢活性的参数。经体重标准化的SUV受身体脂肪和瘦组织比例的影响,这在癌症患者中存在很大变异性。建议用瘦体重(LBM)校正的SUV(记为SUL)来提供更准确、一致和可重复的SUV结果;然而,LBM通常是估算而非测量得到的。鉴于定量PET参数的重要性日益增加,尤其是在比较不同时间的PET研究以临床评估疾病反应以及其在肿瘤学临床试验中的应用时,我们着手评估最初由詹姆斯(1976年)和贾姆哈萨蒂安等人(2005年)推导的常用方程与计算机断层扫描(CT)得出的LBM测量值的对比情况。

方法

对195例成年癌症患者的全身F-FDG PET图像进行回顾性分析。代表性肝脏SUV经总体重标准化。基于PET/CT研究的CT成分(LBM)对LBM进行定量测定来计算SUL,并与方程估算的SUL进行比较。生成SUV - SUL差异的布兰德和奥特曼图。

结果

这一接受常规治疗的连续患者样本(男性n = 96;女性n = 99)体重(38 - 127千克)和体重指数(BMI)(14.7 - 47.2千克/平方米)各不相同。LBM与体重弱相关(男性,r = 0.32;女性,r = 0.22),因此SUV和SUL也弱相关(男性,r = 0.24;女性,r = 0.11)。方程被证明不足以评估LBM。詹姆斯方程估算的LBM在男性中显示出平均偏差(与LBM相比LBM高估)(+6.13千克;95%置信区间4.61 - 7.65),在女性中为(+6.32千克;95%置信区间5.26 - 7.39)。贾姆哈萨蒂安的方程表现同样不佳。

结论

基于CT的LBM测定纳入了PET/CT研究时患者当前的身体组成,所获取的信息可为医护团队提供信息,以便更准确地确定FDG摄取值,实现多次扫描和治疗过程的可比性,并为在临床试验中使用实体瘤PET反应标准(PERCIST)提供有力依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/9a7ec2e6bf0c/fonc-12-812777-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/28095670f428/fonc-12-812777-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/c25b656d1f4a/fonc-12-812777-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/9024dcd397d0/fonc-12-812777-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/4f15ce05043b/fonc-12-812777-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/a24cad976388/fonc-12-812777-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/9a7ec2e6bf0c/fonc-12-812777-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/28095670f428/fonc-12-812777-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/c25b656d1f4a/fonc-12-812777-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/9024dcd397d0/fonc-12-812777-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/4f15ce05043b/fonc-12-812777-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/a24cad976388/fonc-12-812777-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffbb/9302197/9a7ec2e6bf0c/fonc-12-812777-g006.jpg

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