Poster Presentation 5th International Symposium on Phaeochromocytoma and Paraganglioma 2017

Quantification of glucose metabolic rate and 18F-FDG kinetics in pheochromocytoma and paraganglioma by using dynamic PET/CT scanning   (#62)

Anouk van Berkel 1 , Dennis D Vriens 2 , Eric E Visser 3 , Marcel M.J.R. Janssen 3 , Martin M Gotthardt 3 , Jacques J.W.M. Lenders 1 4 , Ad A.R.M.M. Hermus 1 , Lioe-Fee L.F. Geus-Oei 2 5 , Henri H.J.L.M. Timmers 1
  1. Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, Nijmegen, The Netherlands
  2. Department of Radiology, Leiden University Medical Center, Nijmegen, The Netherlands
  3. Department of Radiology & Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
  4. Department of Internal Medicine III, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
  5. MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands

Background: Static single timeframe 18F-FDG PET is useful for the localization and functional characterization of primary and metastatic pheochromocytoma and paraganglioma (PPGL). 18F-FDG uptake varies between PPGL genotypes and highest standardized uptake values (SUVs) are observed in case of succinate dehydrogenase (SDH) mutations, probably related to enhanced aerobic glycolysis. The exact determinants of 18F-FDG accumulation remain unknown. We performed multi timeframe dynamic PET scanning to assess in vivo 18F-FDG kinetics  to investigate whether dynamic PET has added value over static PET for distinguishing between different genotypes.

Methods: Dynamic 18F-FDG PET/CT was done in 26 patients with PPGL. A two-tissue compartment tracer kinetic model assuming irreversible 18F-FDG metabolism was used to estimate transfer rates of 18F-FDG between the vascular/extravascular extracellular space (EES), non-metabolized and metabolized tissue compartments. The derived transfer rates 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 pharmacokinetic linear regression analysis.

Results: Both MRglc and maximum SUVs for cluster 1 (SDHx,VHL) tumors were significantly higher than those for cluster 2 (RET, NF1) (P<0.01) and sporadic tumors (P<0.01, P<0.05). Median k3 in cluster 1 was significantly higher than for sporadic tumors (P<0.01). Median Vbfor cluster 1 was significantly higher than for cluster 2 tumors (P<0.01). No statistical differences in K1 and k2 were found between the three groups. Cutoff values for k3 to distinguish between cluster 1 and other tumors was established at 0.071(100% specificity, 100% sensitivity). MRglc significantly correlated with maximum SUV (P=0.001) and k3 (P=0.002).

Conclusion: In vivo metabolic tumor profiling in patients with PPGL can be easily achieved by assessing 18F-FDG kinetics using multi timeframe dynamic PET scanning. SDH-deficient PPGLs can be reliably identified by a high 18F-FDG phosphorylation rate.