Parameters of the Hybrid Method [68Ga]PSMA PET/CT in Different Diagnostic Groups of Patients

Introduction/ Aim: The aim of our study was to assess the parameters of the hybrid imaging method 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET/CT): detection rate, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy in different diagnostic groups of patients.Materials and Methods: We analyzed retrospectively and included in the study a total of 386 patients diagnosed with prostate cancer (PC) in the period 07.2019–01.2021, based on defined inclusion criteria. The patients were divided into four groups: 133 with biochemical recurrence (BHR) after radical therapy (group I); 144 with biochemical progression after radical prostatectomy (RP) (group II); 109 referred for staging of primary PC (group III); and 61 with high-risk PC (group IV).Results and discussion: We reported high specificity (from 97.7% to 100.0%) and PPV (from 98.9% to 100.0%) in all studied diagnostic groups of patients. We reported high values for the sensitivity of the hybrid imaging method in all diagnostic groups of patients (from 92.3% to 98.9%), with lower values found in patients with ISUP grade 5 (group IV) (92.3%) compared to other groups (from 95.3% to 98.9%). NPV was reported from 69.2% to 97.7%, with lower values shown by patients in group IV (69.2%) compared to the first three groups of patients (from 97.1% to 98.8%). The accuracy of the method in all studied groups of patients was reported as high: from 93.4% to 99.3%. Patients with ISUP grade 5 had relatively lower values (93.4%) when compared to other groups (98.2%–99.3%).Conclusion: 68Ga-PSMA PET/CCT is a highly promising imaging modality for PC detection in BHR and staging of primary PC, demonstrating high parametric data in all studied groups of patients. Patients with ISUP grade 5 have the highest detection rate, while other parameters in the same group (NPV, sensitivity, and accuracy) have relatively lower values.