Clinical nuclear medicine · 2013
Evaluation of quantitative criteria for glioma grading with static and dynamic 18F-FDopa PET/CT.
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Nioche Christophe, Soret Marine, Gontier Eric, Lahutte Marion, Dutertre Guillaume, Dulou Renaud, Capelle Laurent, Guillevin Rémy, Foehrenbach Hervé, Buvat Irène
Аннотация
PURPOSE: The aim of this study was to compare various acquisition and processing protocols for noninvasive glioma grading using either static or dynamic (18)F-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, SUV(mean) and SUV(max) were calculated for different acquisition time ranges after injection. For dynamic imaging, the transport rate constant k1 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 SUV(mean) 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 k1 of tumors. In static imaging, the best discrimination based on SUV was obtained for SUV(mean) calculated from a 5-minute acquisition started at 38 minutes after injection.
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, SUV(mean) and SUV(max) were calculated for different acquisition time ranges after injection. For dynamic imaging, the transport rate constant k1 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 SUV(mean) 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 k1 of tumors. In static imaging, the best discrimination based on SUV was obtained for SUV(mean) calculated from a 5-minute acquisition started at 38 minutes after injection.