Diagnostic value of 18F-FDG PET/CT in the follow-up of metastatic uveal melanoma.

To evaluate the accuracy of fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in detecting metastatic uveal melanoma (UM) using both per-patient and per-lesion analyses, while also characterizing lesion detectability across various metastatic sites.

In this retrospective study conducted from January 2011 to September 2024, UM participants underwent PET/CT scans for follow-up or suspected recurrence. The lesion uptake were quantified by maximum standardized uptake value (SUVmax). Pathology and clinical follow-up served as reference standard.

Fifty-five participants (mean age, 49.2 ± 12.7; 26 females) were evaluated, and the average recurrent time was 30.7 months (IQR, 18.0-89.2). On per-patient level, 31 patients (56%) were confirmed to have metastatic lesions through pathology or clinical follow-up, of which 28/31 (90.3%) patients were successfully detected by 18F-FDG PET/CT and 3/31 (9.7%) patients with liver metastases were missed. Seventeen of 31 patients (54.8%) had multiple organ involvement. On per-lesion level, a total of 270 lesions were comfirmed, of which 245 (90.7%) were detected by 18F-FDG PET/CT, including metastasis to liver (103 of 128, 80.5%), bone (64 of 64, 100%), lymph node (24 of 24, 100%), lung (33 of 33, 100%), and other uncommen sites (21 of 21, 100%). The detection ability of 18F-FDG for liver metastases was positively correlated with the diameter of the lesions (r2 = 0.671, p = 0.000). 18F-FDG successfully detected all bone, lymph node, and lung metastases, with 30 of 64 (46.9%) bone metastases showing no changes on CT and 12 of 24 (50%) lymph node metastases being less than 10 mm, making them prone to misdiagnosis on CT.

18F-FDG PET/CT may be a useful diagnostic tool in detecting metastatic UM, especially for early bone metastases and small lymph nodes. Added contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) may be still needed for tiny liver metastases detection.
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Authors

Ma Ma, Guo Guo, Zhao Zhao, Liu Liu, Luo Luo, Xue Xue, Zhou Zhou
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