使用全身 [18F]FDG-PET/CT 成像检测肺癌患者中癌症相关的恶病质：一项多中心研究。
Detection of cancer-associated cachexia in lung cancer patients using whole-body [18F]FDG-PET/CT imaging: A multi-centre study.
发表日期：2024 Aug 27
作者：
Daria Ferrara, Elisabetta M Abenavoli, Thomas Beyer, Stefan Gruenert, Marcus Hacker, Swen Hesse, Lukas Hofmann, Smilla Pusitz, Michael Rullmann, Osama Sabri, Roberto Sciagrà, Lalith Kumar Shiyam Sundar, Anke Tönjes, Hubert Wirtz, Josef Yu, Armin Frille
来源：
Journal of Cachexia Sarcopenia and Muscle
摘要：
癌症相关恶病质 (CAC) 是一种代谢综合征，可导致肺癌患者 (LCP) 产生治疗耐药性和死亡率。 CAC 通常使用临床非影像学标准来定义。鉴于 CAC 的代谢基础以及 [18F]氟-2-脱氧-D-葡萄糖 (FDG)-正电子发射断层扫描 (PET)/计算机断层扫描 (CT) 提供葡萄糖周转定量信息的能力，我们评估了其有用性全身 (WB) PET/CT 成像，作为 LCP 标准诊断检查的一部分，以提供有关 CAC 发生或存在的更多信息。这项多中心研究包括 345 名接受 WB [18F]FDG-用于初始临床分期的 PET/CT 成像。根据体重指数调整的体重减轻分级系统 (WLGS) 用于将 LCP 分类为“无 CAC”（治疗前基线和第一次随访时的 WLGS-0/1：N = 158, 51F/107M）， Dev CAC'（基线处的 WLGS-0/1 和后续的 WLGS-3/4：N = 90、34F/56M）和“CAC”（基线处的 WLGS-3/4：N = 97、31F/ 66M）。对于每个 CAC 类别，使用基线 [18F]FDG 自动图像分割提取腹部和内脏器官、肌肉和脂肪组织的标准化主动脉摄取 () 的平均标准化摄取值 (SUV) 和 CT 定义的体积-PET/CT 图像。对实验室测试的成像和非成像参数进行统计比较。然后训练机器学习 (ML) 模型，根据 LCP 的成像参数将其分类为“无 CAC”、“开发 CAC”和“CAC”。采用 SHapley 附加解释 (SHAP) 分析来确定每位患者 CAC 发展的关键因素。三个 CAC 类别显示 的多器官差异。在所有靶器官中，“CAC”组中的 高于“无 CAC”组 (P < 0.01)，但肝脏和肾脏除外，“CAC”组中的 降低了 5%。 “Dev CAC”队列显示胰腺 (4%)、骨骼肌 (7%)、皮下脂肪组织 (11%) 和内脏脂肪组织 (15%) 的 有小幅但显着的增加。在“CAC”患者中， 与脂肪组织体积之间存在很强的 Spearman 负相关性 (ρ = -0.8)。机器学习模型在基线时以 81% 的准确度识别出“CAC”，突出显示脾脏、胰腺、肝脏和脂肪组织的 作为最相关的特征。在对“Dev CAC”与“无 CAC”进行分类时，模型性能未达到最佳 (54%)。WB [18F]FDG-PET/CT 成像揭示了有和没有 CAC 的 LCP 多器官代谢的分组差异，从而突出了系统性恶病质患者的代谢异常症状。基于回顾性队列，我们的 ML 模型非常准确地识别了 CAC 患者。然而，它在发生 CAC 的患者中的表现并不理想。一项前瞻性、多中心研究已启动，以解决当前回顾性分析的局限性。© 2024 作者。 《恶病质、肌肉减少症和肌肉杂志》由 Wiley periodicals LLC 出版。
Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non-imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole-body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC.This multi-centre study included 345 LCP who underwent WB [18F]FDG-PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into 'No CAC' (WLGS-0/1 at baseline prior treatment and at first follow-up: N = 158, 51F/107M), 'Dev CAC' (WLGS-0/1 at baseline and WLGS-3/4 at follow-up: N = 90, 34F/56M), and 'CAC' (WLGS-3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake () and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [18F]FDG-PET/CT images. Imaging and non-imaging parameters from laboratory tests were compared statistically. A machine-learning (ML) model was then trained to classify LCP as 'No CAC', 'Dev CAC', and 'CAC' based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient.The three CAC categories displayed multi-organ differences in . In all target organs, was higher in the 'CAC' cohort compared with 'No CAC' (P < 0.01), except for liver and kidneys, where in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in of pancreas (+4%), skeletal-muscle (+7%), subcutaneous adipose-tissue (+11%), and visceral adipose-tissue (+15%). In 'CAC' patients, a strong negative Spearman correlation (ρ = -0.8) was identified between and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'.WB [18F]FDG-PET/CT imaging reveals groupwise differences in the multi-organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi-centre study has been initiated to address the limitations of the present retrospective analysis.© 2024 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.