BACKGROUND

Sarcopenia is essential in managing advanced stage (III-IV) non-small cell lung cancer (NSCLC) but is laborious to diagnose using currently available method. This study aimed to establish a simple approach to predict sarcopenia using F-FDG PET/CT parameters and clinical characteristics and determine their roles in prognostication in advanced stage NSCLC.

METHODS

Untreated 202 NSCLC patients with stage III-IV were retrospectively reviewed. Sarcopenia was defined using the skeletal muscle index (SMI) measured at the third lumbar vertebra (L3). F-FDG PET/CT metabolic parameters of maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis of the primary tumor (SUVmax_T, MTV_T, and TLG_T) and of whole-body lesions (MTV_WB and TLG_WB) were measured. Besides, SUVmax of the psoas major muscle (SUVmax_Muscle) was measured at the L3 level. The diagnostic endpoint was the probability of sarcopenia, and the survival endpoints included progression-free survival (PFS) and overall survival (OS).

RESULTS

Among the enrolled 202 patients, 82 (40.6%) were diagnosed with sarcopenia. Higher age, male, lower BMI, and lower SUVmax_Muscle were correlated with a higher incidence of sarcopenia (P < 0.05), while age, sex, BMI, and SUVmax_Muscle were independently predictive of sarcopenia, and thus were utilized to construct a nomogram model. Multivariate Cox regression analysis revealed that sarcopenia score derived from the nomogram model, sarcopenia, stage, and TLG_WB were independently predictive of both PFS and OS.

CONCLUSION

The incidence of sarcopenia increased with declining SUVmax_Muscle in advanced stage NSCLC. Our model using age, sex, BMI, and SUVmax_Muscle might be substituted for the complicated measurement of SMI. After adjustment by stage and TLG_WB, both sarcopenia score and sarcopenia were found to be independently predictive of PFS and OS.