KMID : 1202020090020020120
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Journal of Korean Thyroid Association 2009 Volume.2 No. 2 p.120 ~ p.127
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Usefulness of SUV Ratio for Differentiating Benign from Malignant Focal Thyroid Lesions Incidentally Detected by F-18 FDG PET/CT: Comparison with SUV Analysis
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Kim Bum-Sahn
Kang Won-Jun Oh So-Won Lee Dong-Soo Chung June-Key Lee Myung-Chul
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Abstract
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Background and Objectives :We investigated the usefulnesses of SUV and SUV ratios in terms of discriminating focal thyroid lesions incidentally detected by F-18 FDG PET/CT (FDG PET) in patients with various cancers.
Materials and Methods : Among 2,635 patients, 20 patients with 22 thyroid incidentalomas (benign vs. malignant=10 vs. 12) on FDG PET were assessed. Maximal SUV was measured by drawing a ROI on thyroid lesions, on contralateral thyroid lobes, and on the liver. SUV ratio was calculated as the thyroid lesions versus the contralateral lobes and liver.
Results:A marginally significant difference in maximal SUV was found between malignant and benign nodules (4.67¡¾2.78 vs. 2.59¡¾1.84, p=0.05). However, more significant differences were found using SUV ratio to normal organs than that of SUV. Malignant nodules had a higher SUV ratio versus liver than benign nodules (2.09¡¾0.96 vs. 1.13¡¾0.65, p=0.012). The SUV ratio of lesions versus contralateral lobes were found to significantly predict the presence of malignancy (3.74¡¾2.19 vs. 1.89¡¾1.17, p=0.027). By ROC curve analysis, the best SUV value cut-off for the prediction of malignancy was 2.42 with a sensitivity of 91.7% and a specificity of 60.0% (AUC=0.758). Similarly, the best SUV ratio cut-off versus the liver was 1.34 with a sensitivity of 91.7% and a specificity of 70.0% (AUC=0.808). But, there was no significant difference between areas of the ROC curves (p=0.736).
Conclusion:The SUV ratio had a tendency to discriminate benign from malignant thyroid incidentalomas better than the SUV method. Further investigations with larger number of patients are needed.
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KEYWORD
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FDG PET/CT, Thyroid incidentaloma, SUV, SUV ratio
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