Abstract
BACKGROUND: Our previous studies have found that certain cancer patients with sarcopenia tend to present with lower glucometabolism within skeletal muscles. This study aimed to investigate the association between sarcopenia and (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) along with clinicopathological factors in elderly patients with treatment-naïve malignant tumors. METHODS: Clinicopathological and (18)F-FDG PET/CT characteristics of 1,024 elderly patients with newly diagnosed malignancies [638 men, 386 women; median age: 68 years (range, 60-99 years)] were retrospectively collected. Skeletal muscle index (SMI) was calculated using skeletal muscle area (SMA) measured at the 3(rd) lumbar (L3) level normalized for height. Maximum standardized uptake value (SUVmax) of the psoas muscle at the L3 level was measured. The association between sarcopenia and (18)F-FDG PET/CT parameters along with clinicopathological factors was analyzed. RESULTS: Among 1,024 patients, 459 patients (44.8%) were diagnosed with sarcopenia. Sarcopenia was more common in males (P<0.001), patients with low body mass index (BMI) (P=0.013), smoking history (P=0.007), diabetes (P=0.035), poor Eastern Cooperative Oncology Group (ECOG) status (P<0.001), multiple primary tumors (P=0.001), and low SUVmax of muscle (P<0.001). Sex (P<0.001), BMI (P=0.028), ECOG status (P<0.001), number of primary tumors (P=0.001), and SUVmax of muscle (P<0.001) were independent predictors of sarcopenia and utilized to construct a predictive nomogram. Furthermore, division by sex revealed that BMI (P=0.021), ECOG status (P<0.001), number of primary tumors (P<0.001), and SUVmax of muscle (P<0.001) independently predicted sarcopenia in males, while diabetes (P=0.028) and SUVmax of muscle (P<0.001) were independent predictors in females. CONCLUSIONS: Regardless of sex, SUVmax of muscle can independently predict sarcopenia in elderly patients with newly diagnosed malignancies. As an alternative to complex SMI measurements, a nomogram model incorporating SUVmax of muscle can effectively predict the likelihood of sarcopenia.