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
{"title":"Detection of cancer-associated cachexia in lung cancer patients using whole-body [<sup>18</sup>F]FDG-PET/CT imaging: A multi-centre study.","authors":"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","doi":"10.1002/jcsm.13571","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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 [<sup>18</sup>F]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.</p><p><strong>Methods: </strong>This multi-centre study included 345 LCP who underwent WB [<sup>18</sup>F]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 (<SUV<sub>aorta</sub>>) and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [<sup>18</sup>F]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.</p><p><strong>Results: </strong>The three CAC categories displayed multi-organ differences in <SUV<sub>aorta</sub>>. In all target organs, <SUV<sub>aorta</sub>> was higher in the 'CAC' cohort compared with 'No CAC' (P < 0.01), except for liver and kidneys, where <SUV<sub>aorta</sub>> in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in <SUV<sub>aorta</sub>> 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 <SUV<sub>aorta</sub>> and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting <SUV<sub>aorta</sub>> of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'.</p><p><strong>Conclusions: </strong>WB [<sup>18</sup>F]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.</p>","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":null,"pages":null},"PeriodicalIF":8.9000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cachexia, Sarcopenia and Muscle","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jcsm.13571","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.
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.