FDG-PET and CT characterization of adrenal lesions in cancer patients
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Fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) may differentiate benign from malignant adrenal lesions. In this study, standardized uptake values (SUVs), visual interpretation, and computed tomography (CT) data were correlated with the final diagnosis to determine the contribution of adrenal FDG-PET in patients with known non-adrenal cancer.
Ninety-two patients with adrenal lesions on CT underwent FDG-PET. Eighty adrenals in 74 patients met the inclusion criteria (PET scan within 4 weeks of CT plus >1 year of follow-up after PET scan with repeat CT or biopsy for final diagnosis). CT was considered positive for metastases (CT+) based on two of the following three criteria: >4 cm, Hounsfield units (HU) >30, and delayed contrast enhancement. Lesions with <2 cm, with HU <20, and showing no enhancement were considered benign (CT−). Remaining lesions were considered indeterminate (CT-Ind). Visually, adrenal uptake exceeding liver uptake was considered PET positive (PET+). Diagnosis of metastases was based on biopsy or interval CT growth (unchanged >1 year=benign). SUVmax and SUVavg were calculated from a 4×4 pixel region of interest drawn from CT, PET, and fused images. A receiver operator curve (ROC) determined the SUV with the best sensitivity and specificity.
Overall, PET was 93% sensitive and 96% specific for metastases. A SUVmax of 3.4 was 95% sensitive and 86% specific. A SUVavg of 3.1 was 95% sensitive and 90% specific. There was no significant difference between visual interpretation and SUV (SUVmax or SUVavg). Among CT+ and CT− lesions, PET was 100% sensitive and 96% specific; CT was 86% sensitive and 100% specific. In the CT-Ind group, PET was 88% sensitive and 96% specific.
PET accurately characterized adrenal lesions. Visual interpretation was as accurate as SUV. FDG-PET was most useful in the 52.5% of cancer patients with inconclusive adrenal lesions on CT.
KeywordsAdrenal lesions FDG-PET PET/CT Adrenal metastases Image fusion
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