This prospective study aimed to (1) compare the diagnostic performance of ⁶⁸Ga-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.

Purpose: This study aimed to explore metabolic tumor volume (MTV) as assessed by 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG–PET/CT) and understand its biological meaning in patients with non–small cell lung cancer (NSCLC) exposed to immune checkpoint blockers (ICB). Experimental Design: In this study, patients with advanced NSCLC and a positive PET scan within 42 days of first-line treatment were enrolled in 11 institutions across four countries. Total MTV (tMTV) was analyzed, with a 42% maximum standardized uptake value threshold. Survival was analyzed according to high tMTV (≥median). Plasma proteomic profile, whole exome, transcriptome, and other analyses were performed on monocentric cohorts to explore its biological correlates. Results: Of the 518 patients included, 167 received ICBs, 257 had chemotherapy plus ICBs, and 94 had chemotherapy. Median tMTV was 99 cm3. Median overall survival (OS) for patients with high tMTV treated with ICBs was 11.4 vs. 29.6 months (P < 0.0012) for those with low tMTV. In patients who received chemotherapy–ICB, tMTV did not correlate with OS (P = 0.099). In patients with programmed death-ligand 1 (PD-L1) ≥1% and high tMTV, chemotherapy–ICB combination was associated with longer OS compared with ICBs alone (20 vs. 11.4 months; P = 0.026), while no survival differences were observed in the low tMTV group. High tMTV correlated (and its detrimental effect seems to be driven) with a specific proteomic profile and increase in genomic instability. Conclusions: Our analysis indicates high tMTV is linked to an increase in systemic inflammation, specific cytokines production, and chromosomal instability. tMTV may serve as one of the biomarkers to select the best upfront strategy in patients with PD-L1–positive advanced NSCLC.

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 ⁶⁸Ga-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 ⁶⁸Ga-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 ⁶⁸Ga-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-R²: 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.