Metabolic Subtyping of Pheochromocytoma and Paraganglioma by ¹⁸F-FDG Pharmacokinetics Using Dynamic PET/CT Scanning

Abstract

Static single–time-frame ¹⁸F-FDG PET/CT is useful for the localization and functional characterization of pheochromocytomas and paragangliomas (PPGLs). ¹⁸F-FDG uptake varies between PPGLs with different genotypes, and the highest SUVs are observed in cases of succinate dehydrogenase ( SDH) mutations, possibly related to enhanced aerobic glycolysis in tumor cells. The exact determinants of ¹⁸F-FDG accumulation in PPGLs are unknown. We performed dynamic PET/CT scanning to assess whether in vivo ¹⁸F-FDG pharmacokinetics has added value over static PET to distinguish different genotypes. Methods: Dynamic ¹⁸F-FDG PET/CT was performed on 13 sporadic PPGLs and 13 PPGLs from 11 patients with mutations in SDH complex subunits B and D, von Hippel-Lindau ( VHL), RET, and neurofibromin 1 ( NF1). Pharmacokinetic analysis was performed using a 2-tissue-compartment tracer kinetic model. The derived transfer rate-constants for transmembranous glucose flux ( K ₁ [in], k ₂ [out]) and intracellular phosphorylation ( k ₃), along with the vascular blood fraction (V _b), were analyzed using nonlinear regression analysis. Glucose metabolic rate (MR _glc) was calculated using Patlak linear regression analysis. The SUV _max of the lesions was determined on additional static PET/CT images. Results: Both MR _glc and SUV _max were significantly higher for hereditary cluster 1 ( SDHx, VHL) tumors than for hereditary cluster 2 ( RET, NF1) and sporadic tumors ( P < 0.01 and P < 0.05, respectively). Median k ₃ was significantly higher for cluster 1 than for sporadic tumors ( P < 0.01). Median V _b was significantly higher for cluster 1 than for cluster 2 tumors ( P < 0.01). No statistically significant differences in K ₁ and k ₂ were found between the groups. Cutoffs for k ₃ 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). MR _glc significantly correlated with SUV _max ( P = 0.001) and k ₃ ( 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.