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Evaluation of Quantitative Criteria for Glioma Grading With Static and Dynamic 18F-FDopa PET/CT

Nioche, Christophe PhD*; Soret, Marine PhD*; Gontier, Eric MD*; Lahutte, Marion MD*; Dutertre, Guillaume MD*; Dulou, Renaud MD*; Capelle, Laurent MD; Guillevin, Rémy MD, PhD; Foehrenbach, Hervé MD*; Buvat, Irène PhD

doi: 10.1097/RLU.0b013e318279fd5a
Original Articles

Purpose The aim of this study was to compare various acquisition and processing protocols for noninvasive glioma grading using either static or dynamic 18F-FDopa PET.

Methods Dynamic studies were performed in 33 patients. Based on histopathological analysis, 18 patients had a high-grade (HG) tumor and 15 patients had a low-grade (LG) tumor. For static imaging, SUVmean and SUVmax were calculated for different acquisition time ranges after injection. For dynamic imaging, the transport rate constant k 1 was calculated according to a compartmental kinetic analysis using an image-derived input function.

Results With the use of a 5-minute static imaging protocol starting at 38 minutes after injection, newly diagnosed HG tumors could be distinguished from LG tumors with a sensitivity of 70% and a specificity of 90% with a threshold of SUVmean of 2.5. In recurrent tumors, a sensitivity of 100% and a specificity of 80% for identifying HG tumors were obtained with a threshold set to 1.8. Dynamic imaging only slightly, but nonsignificantly, improved differential diagnosis.

Conclusions Static and dynamic imaging without blood sampling can discriminate between LG and HG for both newly diagnosed and recurrent gliomas. In dynamic imaging, excellent discrimination was obtained by considering the transport rate constant k 1 of tumors. In static imaging, the best discrimination based on SUV was obtained for SUVmean calculated from a 5-minute acquisition started at 38 minutes after injection.

From the *Hôpital d’Instruction des Armées du Val-de-Grâce 74, bd du Port Royal; †UMR 678 INSERM AP-HP Groupe Hospitalier Pitié-Salpêtrière, Paris; and ‡UMR 8165 CNRS Imagerie et Modélisation en Neurobiologie et Cancérologie Orsay, France.

Received for publication June 2, 2012; revision accepted September 27, 2012.

Conflicts of interest and sources of funding: none declared.

Reprints: Christophe Nioche, PhD, (Fédération d’Imagerie Médicale), Hôpital d’Instruction des Armées du Val-de-Grâce 74, bd du Port Royal, 75005 Paris, France. E-mail:

© 2013 Lippincott Williams & Wilkins, Inc.