Metabolic subtyping of pheochromocytoma and paraganglioma by 18F-FDG pharmacokinetics using dynamic PET/CT scanning

Abstract

Background: Static single timeframe 18F-FDG PET/CT is useful for the localization and functional characterization of pheochromocytoma and paraganglioma (PPGL). 18F-FDG-uptake varies between PPGLs with different genotypes and highest standardized uptake values (SUVs) are observed in case of succinate dehydrogenase (SDH) mutations, possibly related to enhanced aerobic glycolysis in tumor cells. The exact determinants of 18F-FDG accumulation in PPGL are unknown. We performed dynamic PET/CT scanning to assess in vivo 18F-FDG pharmacokinetics to investigate whether it has added value over static PET to distinguish different genotypes. Methods: Dynamic 18F-FDG PET/CT was performed in 13 sporadic PPGLs and 13 PPGLs from 11 patients with hereditary mutations in SDHA/B/D, von Hippel-Lindau (VHL), RET and neurofibromin 1 (NF1). Pharmacokinetic analysis was performed using a two-tissue compartment tracer kinetic model. The derived transfer rate-constants for transmembranous glucose flux (K1 (in), k2 (out)) and intracellular phosphorylation (k3) along with the fractional blood volume (Vb) were analyzed using non-linear regression analysis. Glucose metabolic rate (MRglc) was calculated using Patlak linear regression analysis. Maximum SUVs (SUVmax) of the lesions were determined on additional static PET/CT. Results: Both MRglc and SUVmax for hereditary cluster 1 (SDHx, VHL) tumors were significantly higher than for hereditary cluster 2 (RET, NF1) (P<0.01) and sporadic tumors (P<0.01, P<0.05, respectively). Median k3 in cluster 1 was significantly higher than for sporadic tumors (P<0.01). Median Vb for cluster 1 was significantly higher than for cluster 2 tumors (P<0.01). No statistical significant differences in K1 and k2 were found between the groups. Cutoff values for k3 to distinguish between cluster 1 and other tumors were established at 0.015 min-1 (100% sensitivity, 15,8% specificity) and 0.636 min-1 (100% specificity, 85.7% sensitivity). MRglc significantly correlated with SUVmax (P = 0.001) and k3 (P = 0.002). Conclusion: In vivo metabolic tumor profiling in patients with PPGL can be achieved by assessing 18F-FDG pharmacokinetics using dynamic PET/CT scanning. Cluster 1 PPGLs can be reliably identified by a high 18F-FDG phosphorylation rate.