Preliminary study on the ability of 68Ga-Pentixafor PET/CT to differentiate between adrenal aldosterone-producing adenoma and nonfunctional adenoma

Abstract

Objective: To evaluate the ability of 68Ga-Pentixafor (nuclide ligand imaging agents for chemokine receptor 4) PET/CT to differentiate between aldosterone-producing adenoma (APA) and adrenal nonfunctional adenoma (NFA), and to assess how well this imaging method correlates with clinical features and postoperative outcomes. Methods: This was a cross-sectional study involving 73 APA and 12 NFA patients who received 68Ga-Pentixafor PET/CT imaging at Peking Union Medical College Hospital from August 2018 to October 2021. The receiver operating characteristic (ROC) curve was used to evaluate the differential value of visual analysis and the maximum standard uptake value (SUVmax) of the focus on APA and NFA. The related factors of SUVmax, and its predictive effect on postoperative outcomes were analyzed using Pearson or Spearman analysis and χ2 text. Results: 68Ga-Pentixafor PET/CT imaging was positive in 64 APA patients (sensitivity=87.7%) and negative in all 12 NFA patients (specificity=100%). The area under the ROC curve with SUVmax differentiating APA and NFA was 0.932 (P<0.001). When the SUVmax cut-off point was 6.23, the sensitivity was 80.8% and the specificity was 100%. The SUVmax correlated positively with lesion size (r=0.598) and aldosterone/renin activity ratio (r=0.313) and correlated negatively with potassium level (r=-0.286), renin activity (r=-0.240) and age of diagnosis (r=-0.273) (all P<0.05). Of the patients who underwent adrenalectomy and received more than 6 months of post-surgical follow-up, the clinical complete remission rate was higher for 68Ga-Pentixafor PET/CT imaging-positive patients than imaging-negative patients (24/39 vs. 0/4, P=0.031). Conclusions: 68Ga-Pentixafor PET/CT is effective at differentiating between APA and NFA. The SUVmax of 68Ga-Pentixafor PET/CT correlates with age at onset, lesion size, and the severity of clinical manifestations, and is able to predict postoperative outcomes.