Detection of cancer-associated cachexia in lung cancer patients using whole-body [18F]FDG-PET/CT imaging: A multi-centre study.

IF 8.9 1区 医学 Journal of Cachexia, Sarcopenia and Muscle Pub Date : 2024-08-27 DOI:10.1002/jcsm.13571
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
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Abstract

Background: 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.

Methods: 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 (aorta>) 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.

Results: The three CAC categories displayed multi-organ differences in aorta>. In all target organs, aorta> was higher in the 'CAC' cohort compared with 'No CAC' (P < 0.01), except for liver and kidneys, where aorta> in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in aorta> 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 aorta> and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting aorta> of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'.

Conclusions: 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.

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利用全身[18F]FDG-PET/CT成像检测肺癌患者的癌症相关恶病质:一项多中心研究。
背景:癌症相关恶病质(CAC)是一种导致肺癌患者(LCP)耐药和死亡的代谢综合征。CAC 通常使用临床非成像标准来定义。考虑到 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,我们评估了全身(WB)PET/CT 成像作为 LCP 标准诊断检查的一部分在提供 CAC 发病或存在的额外信息方面的作用:这项多中心研究纳入了 345 名接受 WB [18F]FDG-PET/CT 成像检查以进行初步临床分期的 LCP 患者。根据体重指数调整的体重减轻分级系统(WLGS)将 LCP 分为 "无 CAC"(治疗前和首次随访时基线 WLGS-0/1:N = 158,51F/107M)、"Dev CAC"(基线 WLGS-0/1 和随访时 WLGS-3/4:N = 90,34F/56M)和 "CAC"(基线 WLGS-3/4:N = 97,31F/66M)。对于每个 CAC 类别,使用基线[18F]FDG-PET/CT 图像的自动图像分割,提取腹部和内脏器官、肌肉和脂肪组织的平均标准化摄取值 (SUV) 归一化为主动脉摄取值(主动脉>)和 CT 定义的体积。对来自实验室检测的成像和非成像参数进行了统计比较。然后训练机器学习(ML)模型,根据成像参数将 LCP 分为 "无 CAC"、"Dev CAC "和 "CAC"。采用SHAPLE Additive exPlanations (SHAP)分析来确定导致每位患者CAC发展的关键因素:结果:三类 CAC 在主动脉上显示出多器官差异。在所有目标器官中,与 "无 CAC "相比,"CAC "组群的主动脉>更高("CAC "组群的主动脉>降低了 5%)。Dev CAC "队列中,胰腺(+4%)、骨骼肌(+7%)、皮下脂肪组织(+11%)和内脏脂肪组织(+15%)的主动脉>有小幅但显著的增加。在 "CAC "患者中,主动脉>与脂肪组织体积之间存在强烈的 Spearman 负相关(ρ = -0.8)。机器学习模型识别基线 "CAC "的准确率为 81%,脾脏、胰腺、肝脏和脂肪组织的主动脉>是最相关的特征。在对 "Dev CAC "和 "No CAC "进行分类时,该模型的表现并不理想(54%):结论:WB[18F]FDG-PET/CT 成像揭示了有 CAC 和无 CAC 的 LCP 多器官代谢的组间差异,从而突显了慢性钙化患者的全身代谢异常症状。基于回顾性队列,我们的 ML 模型能准确识别 CAC 患者。然而,该模型在出现 CAC 的患者中的表现并不理想。我们已经启动了一项前瞻性多中心研究,以解决目前回顾性分析的局限性。
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来源期刊
Journal of Cachexia, Sarcopenia and Muscle
Journal of Cachexia, Sarcopenia and Muscle Medicine-Orthopedics and Sports Medicine
自引率
12.40%
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期刊介绍: The Journal of Cachexia, Sarcopenia, and Muscle is a prestigious, peer-reviewed international publication committed to disseminating research and clinical insights pertaining to cachexia, sarcopenia, body composition, and the physiological and pathophysiological alterations occurring throughout the lifespan and in various illnesses across the spectrum of life sciences. This journal serves as a valuable resource for physicians, biochemists, biologists, dieticians, pharmacologists, and students alike.
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