18F-FDG PET/CT Volumetric Parameters are Associated with Tumor Grade and Metastasis in Pancreatic Neuroendocrine Tumors in von Hippel-Lindau Disease.

Journal: Annals Of Surgical Oncology
Published:
Abstract

Background: Approximately 8-17 % of patients with von Hippel-Lindau (VHL) syndrome develop pancreatic neuroendocrine tumors (PNETs), with 11-20 % developing metastases. Tumor grade is predictive of prognosis.

Objective: The aim of this study was to determine if preoperative metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were associated with metastatic disease and tumor grade.

Methods: Sixty-two patients with VHL-associated PNETs prospectively underwent 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT). MTV, TLG, and maximum standardized uptake value (SUVmax) were measured using a semi-automatic method. Surgically resected PNETs were classified according to 2010 World Health Organization tumor grade classification. MTV, TLG, and SUVmax were analyzed by metastatic disease and tumor grade using the Mann-Whitney test.

Results: A total of 88 PNETs were identified by CT and 18F-FDG PET/CT, 10 of which were non-FDG-avid. Histologic grading was available for 20 surgical patients. Patients with metastatic PNETs had a higher TLG (median 25.9 vs. 7.7 mean SUV [SUVmean]*mL; p = 0.0092) compared with patients without metastasis, while patients with grade 2 PNETs had a higher MTV (median 6.9 vs. 2.6 mL; p = 0.034) and TLG (median 41.2 vs. 13.1 SUVmean*mL; p = 0.0035) compared with patients with grade 1 PNETs. No difference in tumor size or SUVmax was observed between the groups.

Conclusions: Patients with metastatic PNETs have a higher TLG compared with patients without metastasis. Grade 2 PNETs have a higher MTV and TLG compared with grade 1 PNETs. Tumor size and SUVmax were not associated with grade. Volumetric parameters on 18F-FDG PET/CT may be useful in detecting higher grade PNETs with a higher malignant potential that may need surgical intervention.

Authors
Kei Satoh, Samira Sadowski, William Dieckmann, Martha Quezado, Naris Nilubol, Electron Kebebew, Dhaval Patel