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This prospective study aimed to (1) compare the diagnostic performance of 68 Ga-PSMA-11 PET/CT with respect to conventional imaging (computed tomography (CT) and bone scintigraphy (BS)) in the primary staging of high-risk prostate cancer (PCa) patients and (2) validate PSMA-PET/CT accuracy in pelvic nodal staging in comparison with postoperative histopathology and assess PSMA-PET/CT's impact on patient management. Sixty castration-sensitive high-risk (ISUP 4-5 and/or PSA > 20 ng/mL and/or cT3) PCa patients eligible for radical prostatectomy were enrolled (median PSA 10.10 [IQR: 6.22-17.95] ng/mL). PSMA-PET/CT, compared with CT, identified nodal (N) and/or distant metastases (M1) in 56.7% (34/60) vs. 13.3% (8/60) ( p < 0.001) of patients: N + 45% vs. 13.3% ( p < 0.001), M1a 11.7% vs. 1.7% ( p = 0.03), M1b 23.3% vs. 1.7% ( p < 0.001). Compared with BS, PSMA-PET/CT localized unknown skeletal metastases in 15% (9/60) of cases, with no false negative findings. Overall, PSMA-PET/CT led to a TNM upstaging in 45.0% (27/60) of cases, with no evidence of downstaging, resulting in a change in management in up to 28.8% (17/59) of patients. Compared with histopathology data ( n = 32 patients), the per-patient accuracy of PSMA-PET/TC for detecting pelvic nodal metastases was 90.6%. Overall, the above evidence supports the use of PSMA-PET/CT in the diagnostic workup of high-risk prostate cancer staging.

The aim of this study was to investigate whether the favorable characteristics of novel digital PET/CT (dPET) scanners compared to analog systems (aPET) could translate into an improved disease localization in prostate cancer (PCa) patients with early biochemical recurrence/persistence (BCR/BCP). A retrospective analysis was conducted on 440 consecutive analog ( n = 311) or digital ( n = 129) 68 Ga-PSMA-11 PET/CT scans performed in hormone-sensitive ADT-free PCa patients with early-BCR/BCP (PSA at PET ≤ 2.0 ng/mL), previously treated with radical intent (radical-prostatectomy/radiotherapy). dPET showed a higher positivity rate compared to aPET (48.8% [63/129] vs. 37.3% [116/311], p = 0.03), despite the slightly lower median PSA value of the dPET cohort (0.33 [IQR: 0.26-0.61] vs. 0.55 [IQR: 0.40-0.85] ng/mL, p < 0.01). dPET detection rate was higher in both PSA ranges 0.2-0.5 ng/mL (39.0% [32/82] vs. 25.2% [34/135], p = 0.03) and 0.5-1.0 ng/mL (63.2% [24/38] vs. 40.8% [53/130], p = 0.02), but not for PSA ≥ 1.0 ng/mL. dPET detected a higher per patient median number of pathologic findings (PSMA-RADS ≥ 3) and multi-metastatic cases (>3 lesions) among N1/M1-positive scans (21.7% [10/46] vs. 8.6% [9/105], p = 0.03). Moreover, the proportion of uncertain findings among pathological lesions was significantly lower for dPET than aPET (24.4% [39/160] vs. 38.5% [60/156], p = 0.008). Overall, 68 Ga-PSMA-11 dPET showed a better performance compared to aPET, resulting in a higher scan-positivity rate, a higher number of detected pathological lesions, and a lower rate of uncertain findings.

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative 68 Ga-PSMA-11 PET/CT specimen images. Six ( n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of 68 Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm ( n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97-99%), recall (68-81%), Dice coefficient (80-88%) and Jaccard index (67-79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room.

Prostate-specific-membrane-antigen/positron-emission-tomography (PSMA-PET) can accurately detect disease localizations in prostate cancer (PCa) patients with early biochemical recurrence/persistence (BCR/BCP), allowing for more personalized image-guided treatments in oligometastatic patients with major impact in the case of bone metastases (BM). Therefore, this study aimed to identify predictors of BM at PSMA-PET in early-BCR/BCP hormone-sensitive PCa (HSPC) patients, previously treated with radical intent (radiotherapy or radical prostatectomy ± salvage-radiotherapy (SRT)). A retrospective analysis was performed on 443 68 Ga-PSMA-11-PET/CT scans. The cohort median PSA at PET-scan was 0.60 (IQR: 0.38-1.04) ng/mL. PSMA-PET detection rate was 42.0% (186/443), and distant lesions (M1a/b/c) were found in 17.6% (78/443) of cases. BM (M1b) were present in 9.9% (44/443) of cases, with 70.5% (31/44) showing oligometastatic spread (≤3 PSMA-positive lesions). In the multivariate binary logistic regression model (accuracy: 71.2%, Nagelkerke-R 2 : 13%), T stage ≥ 3a (OR: 2.52; 95% CI: 1.13-5.60; p = 0.024), clinical setting (previous SRT vs. first-time BCR OR: 2.90; 95% CI: 1.32-6.35; p = 0.008), and PSAdt (OR: 0.93; 95% CI: 0.88-0.99; p = 0.026) were proven to be significant predictors of bone metastases, with a 7% risk increment for each single-unit decrement of PSAdt. These predictors could be used to further refine the indication for PSMA-PET in early BCR/BCP HSPC patients, leading to higher detection rates of bone disease and more personalized treatments.