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

• Published in: The Journal of nuclear medicine (1978) Vol. 60; no. 6; pp. 745 - 751
• Main Authors: van Berkel, Anouk, Vriens, Dennis, Visser, Eric P, Janssen, Marcel JR, Gotthardt, Martin, Hermus, Ad RMM, de Geus-Oei, Lioe-Fee, Timmers, Henri JLM
• Format: Journal Article
• Language: English
• Published: New York Society of Nuclear Medicine 01.06.2019
• Subjects: Clusters; Computed tomography; Fluorine isotopes; Genotypes; Glucose; Glycolysis; Lesions; Localization; Medical imaging; Metabolic rate; Metabolism; Mutation; Neoplasia; Neuroendocrine tumors; Neurofibromin 1; Nonlinear analysis; Oncology; Paraganglioma; Pharmacokinetics; Pharmacology; Pheochromocytoma; Phosphorylation; Positron emission; Positron emission tomography; Regression analysis; Scanning; Sensitivity; Statistical analysis; Succinate dehydrogenase; Tomography; Tumor cells; Tumors; VHL protein
• ISSN: 0161-5505; 1535-5667
• DOI: 10.2967/jnumed.118.216796

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 (K1 [in], k2 [out]) and intracellular phosphorylation (k3), along with the vascular blood fraction (Vb), were analyzed using nonlinear regression analysis. Glucose metabolic rate (MRglc) was calculated using Patlak linear regression analysis. The SUVmax of the lesions was determined on additional static PET/CT images. Results: Both MRglc and SUVmax 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 k3 was significantly higher for cluster 1 than for sporadic tumors (P < 0.01). Median Vb was significantly higher for cluster 1 than for cluster 2 tumors (P < 0.01). No statistically significant differences in K1 and k2 were found between the groups. Cutoffs 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.