Establishment and prospective validation of an SUV(max) cutoff value to discriminate clinically significant prostate cancer from benign prostate diseases in patients with suspected prostate cancer by (68)Ga-PSMA PET/CT: a real-world study

Background and Aims: The aims of this study were to establish a maximum standardized uptake value (SUV(max)) cutoff to discriminate clinically significant prostate cancer (csPCa) from benign prostate disease (BPD) by (68)Ga-labeled prostate-specific membrane antigen ((68)Ga-PSMA-11) positron emission tomography/computed tomography (PET/CT) in patients with suspected prostate cancer (PCa), and to perform a prospective real-world validation of this cutoff value. Methods: The study included a training cohort to identify an SUV(max) cutoff value and a prospective real-world cohort to validate it. A retrospective analysis assessed 135 patients with suspected PCa in a large tertiary care hospital in China who underwent (68)Ga-PSMA-11 PET/CT. All patients were suspected of having PCa based on symptoms, digital rectal examination (DRE), total prostate-specific antigen (tPSA) level, and multiparameter magnetic resonance imaging (mpMRI). The (68)Ga-PSMA PET/CT results were evaluated using histopathological results from transrectal ultrasound-guided 12-core biopsy with necessary targeted biopsy as references. Patients with Gleason scores (GS) ≥7 from the biopsy results were diagnosed with csPCa, and patients with negative biopsy and follow-up results were diagnosed with BPD. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal SUV(max) cutoff value. The cutoff value was prospectively validated in 58 patients with suspected PCa. The diagnostic benefits of the cutoff value for clinical decision making were also evaluated. Results: According to ROC curve analysis, the most appropriate SUV(max) cutoff value for discriminating csPCa from BPD was 5.30 (sensitivity, 85.85%; specificity, 86.21%; area under the curve [AUC], 0.893). The cutoff achieved a sensitivity of 83.33%, a specificity of 81.25%, a positive predictive value (PPV) of 92.11%, a negative predictive value (NPV) of 65.00%, and an accuracy of 82.76% in the prospective validation cohort. Metastases were used as an indicator to reduce false negative results in patients with SUV(max) ≤ 5.30. In patients without metastases, an SUV(max) value of 5.30 was also the best cutoff to diagnose localized csPCa (sensitivity, 80.43%; specificity, 86.21%; AUC, 0.852). The cutoff discriminated localized csPCa from BPD with a sensitivity of 76.19%, a specificity of 81.25%, a PPV of 84.21%, an NPV of 72.22%, and an accuracy of 78.38% in the prospective validation cohort. The cutoff, combined with metastases, achieved an accuracy of 89.12% in all patients, increasing accuracy by 8.29% and reducing equivocal results compared with manual reading. There was a strong correlation between SUV(max) and PSMA expression (r(s) = 0.831, P < 0.001) and a moderate correlation between SUV(max) and GS (r(s) = 0.509, P < 0.001). The PSMA expression and SUV(max) values of patients with csPCa were significantly higher than those of patients with BPD (P < 0.001). Conclusion: We established and prospectively validated the best SUV(max) cutoff value (5.30) for discriminating csPCa from BPD with high accuracy in patients with suspected PCa. 5.30 is an effective cutoff to discriminate csPCa patients with or without metastases. The cutoff may provide a potential tool for the precise identification of csPCa by (68)Ga-PSMA PET/CT, ensuring high accuracy and reducing equivocal results.