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

Background Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is defined based on clinical non-imaging criteria. We evaluated the usefulness of whole-body (WB) [¹⁸F]fluoro-2-deoxy-D-glucose (FDG)-PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset of CAC.

Methods This multi-center study retrospectively 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 and first follow-up), ‘Dev CAC’ (WLGS-0/1 at baseline, WLGS-3/4 at follow-up), and ‘CAC’ (WLGS-3/4 at baseline). For each CAC category mean standardized uptake values (SUV) normalized to aorta uptake (< SUVaorta>) and CT-defined volumes were extracted for abdominal organs, muscles, and adipose tissue using automated image segmentation of baseline 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’, ‘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<SUVaorta>. In all target organs,<SUVaorta>was higher in the ‘CAC’ cohort compared with ‘No CAC’ (P<0.01), except for liver and kidneys, where<SUVaorta>in ‘CAC’ was reduced by 5%. In ‘CAC’ patients, a strong negative Spearman correlation (ρ=-0.8) was identified between<SUVaorta>and volumes of adipose tissue. The machine-learning model identified ‘CAC’ at baseline with 81% of accuracy. The model performance was suboptimal (54%) when classifying ‘Dev CAC’ versus ‘No CAC’.

Conclusions WB [¹⁸F]FDG-PET/CT imaging revealed groupwise differences in the multi-organ metabolism of LCP with or without CAC, highlighting systemic metabolic aberrations in patients with CAC. Our ML model identified LCP with CAC with good accuracy. However, its performance in LCP developing CAC was suboptimal. A prospective, multi-center study has been initiated to address these limitations.

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Artikel online veröffentlicht:
18. März 2025