Multimodality characterization of lipid‑poor adenomas: diagnostic performance of computed tomography, magnetic...

Adrenal lesions exhibit a wide spectrum of imaging features, and overlap between benign and malignant entities may complicate diagnosis. This study aimed to evaluate the diagnostic performance of 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT), CT, and magnetic resonance imaging (MRI) in the characterization of adrenal lesions, with particular emphasis on lipid-poor adenomas.

In this retrospective study, 559 adrenal lesions from 477 patients (2012-2024) were analyzed. Lesions were evaluated using PET/CT, unenhanced/contrast-enhanced CT attenuation, and chemical-shift MRI signal changes, based on data availability. Lipid-poor adenomas were defined as lesions with unenhanced CT attenuation > 10 Hounsfield units and no significant signal loss on chemical-shift MRI.

The mean maximum standardized uptake value (SUVmax) for lipid-poor adenomas was 4.73 ± 1.78, which was significantly higher than the 3.13 ± 1.15 observed in the non-lipid-poor group (P = 0.002). Similarly, the adrenal-to-liver SUVmax ratio was significantly elevated in the lipid-poor group (1.62 ± 0.52) compared with the non-lipid-poor group (1.19 ± 0.31) (P = 0.009). The optimal cut-off value was determined as 8.4 for lipid-poor lesions and 5.4 for non-lipid-poor lesions, supporting the necessity of lesion-specific thresholds in adrenal characterization.

Lipid-poor adenomas are metabolically more active and exhibit higher SUVmax values than non-lipid-poor adenomas. Different modalities should be considered when evaluating lipid-poor adenomas. PET/CT is highly sensitive for malignancy detection but may yield false positives in lipid-poor adenomas.

Accurate differentiation of lipid-poor adenomas from malignant adrenal lesions is essential to avoid unnecessary surgery and invasive procedures. Our findings indicate that a multimodality imaging approach combining PET/CT, CT, and MRI improves diagnostic reliability, reduces misclassification, and supports optimal clinical decision-making.