Effectiveness of prolonged fasting 18f-FDG PET-CT in the detection of cardiac sarcoidosis
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The Japanese Ministry of Health and Welfare guidelines (JMHWG) are currently the standard used to diagnose cardiac sarcoidosis. JMHWG incorporate 67Gallium scintigraphy as a minor criterion, while fasting 18fluorine-2-fluro-2-deoxy-d-glucose (FDG) PET is not included. As there is no published data comparing the accuracy of prolonged fasting FDG PET-CT (PF-PET) and Gallium scintigraphy for detecting active cardiac sarcoidosis, we sought to compare these two modalities.
Methods and Results
We retrospectively reviewed medical records and nuclear images of 76 patients with suspected cardiac sarcoid who had either PF-PET or Gallium scintigraphy between January 2004 and August 2008. Eleven patients were excluded due to inadequate fasting for PF-PET, incomplete records or diagnosis other than sarcoid. Cardiac catheterizations, electrocardiogram interpretations, echocardiography reports, pathology reports, therapeutic interventions, and follow-up findings were correlated to PF-PET and Gallium scintigraphy results. Nuclear images of all patients including controls were reviewed independently by two experienced nuclear physicians blinded to results. Using JMHWG as reference standard, sensitivity, specificity, and accuracy of PF-PET were 85%, 90%, and 86.7% and for Gallium scintigraphy were 15%, 80%, and 42.8%.
Relative to Gallium scintigraphy, PF-PET appears to provide greater accuracy for detecting cardiac sarcoidosis. Our findings also highlight the importance of revising JMHWG to incorporate PF-PET and the importance of adequate prolonged fasting prior to FDG PET imaging.
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- Effectiveness of prolonged fasting 18f-FDG PET-CT in the detection of cardiac sarcoidosis
Journal of Nuclear Cardiology
Volume 16, Issue 5 , pp 801-810
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- PET/CT imaging
- fluorodeoxyglucose (FDG)
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- Author Affiliations
- 1. Department of Nuclear Medicine, Medical University of South Carolina, room # 3214, 25 Courtenay drive, Charleston, SC, 29401, USA
- 2. Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC, USA