Sarcopenia tremendously impacts the quality of life but remains debatable in prognostication in treatment-naive patients with non-small cell lung cancer (NSCLC). Hence, this study aimed to find a clinically feasible approach using 18F-FDG PET/computed tomography (CT) imaging parameters and clinical characteristics to predict sarcopenia and determine independent prognostic factors.

Clinical characteristics and 18F-FDG PET/CT metabolic parameters, including maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis of primary tumor (SUVmax_P, MTV_P, and TLG_P) and combination of whole-body lesions (MTV_C and TLG_C) were collected in 344 treatment-naive patients with NSCLC. Skeletal muscle index at the third lumbar vertebra was calculated to determine sarcopenia. SUVmax of the psoas major muscle (SUVmax_M) was measured at the third lumbar vertebra as well. The diagnostic endpoint is the probability of sarcopenia, and the survival endpoints include progression-free survival (PFS) and overall survival (OS).

Among 344 patients with NSCLC there were 271 patients with adenocarcinoma and 73 with squamous cell carcinoma (SCC). One hundred forty-seven patients (42.7%) were diagnosed with sarcopenia. Higher age, male, lower BMI, SCC, and lower SUVmax_M were correlated with a higher incidence of sarcopenia (P < 0.05), while age, sex and SUVmax_M were independently predictive of sarcopenia. Multivariate Cox-regression analysis revealed that BMI, advanced stage and TLG_C were independent predictors of PFS and OS, while sex was independently predictive of OS.

The incidence of sarcopenia increased with declining SUVmax of muscle. BMI, tumor stage, and TLG_C, but not sarcopenia, were found independently predictive of both PFS and OS.