The aim of the present study is to investigate the synergic role of ⁶⁸Ga-PSMA PET/MRI and ⁶⁸Ga-DOTA-RM2 PET/MRI in prostate cancer (PCa) staging. We present pilot data on twenty-two patients with biopsy-proven PCa that underwent ⁶⁸Ga-PSMA PET/MRI for staging purposes, with 19/22 also undergoing ⁶⁸Gaa-DOTA-RM2 PET/MRI. TNM classification based on image findings was performed and quantitative imaging parameters were collected for each scan. Furthermore, twelve patients underwent radical prostatectomy with the availability of histological data that were used as the gold standard to validate intraprostatic findings. A DICE score between regions of interest manually segmented on the primary tumour on ⁶⁸Ga-PSMA PET, ⁶⁸Ga-DOTA-RM2 PET and on T2 MRI was computed. All imaging modalities detected the primary PCa in 18/19 patients, with ⁶⁸Ga-DOTA-RM2 PET not detecting any lesion in 1/19 patients. In the remaining patients, ⁶⁸Ga-PSMA and MRI were concordant. Seven patients presented seminal vesicles involvement on MRI, with two of these being also detected by ⁶⁸Ga-PSMA, and ⁶⁸Ga-DOTA-RM2 PET being negative. Regarding extraprostatic disease, ⁶⁸Ga-PSMA PET, ⁶⁸Ga-DOTA-RM2 PET and MRI resulted positive in seven, four and five patients at lymph-nodal level, respectively, and at a bone level in three, zero and one patients, respectively. These preliminary results suggest the potential complementary role of ⁶⁸Ga-PSMA PET, ⁶⁸Ga-DOTA-RM2 PET and MRI in PCa characterization during the staging phase.

The aim of the present study is to investigate and compare the performances of ⁶⁸Ga-PSMA and ⁶⁸Ga-DOTA-RM2 PET/MRI in identifying recurrent prostate cancer (PCa) after primary treatment and to explore the association of dual-tracer PET findings with clinical and histopathological characteristics. Thirty-five patients with biochemical relapse (BCR) of PCa underwent ⁶⁸Ga PSMA PET/MRI for restaging purpose, with 31/35 also undergoing ⁶⁸Ga-DOTA-RM2 PET/MRI scan within 16 days (mean: 3 days, range: 2–16 days). Qualitative and quantitative image analysis has been performed by comparing ⁶⁸Ga-PSMA and ⁶⁸Ga-DOTA-RM2 PET/MRI findings both on a patient and lesion basis. Clinical and instrumental follow-up was used to validate PET findings. Fisher’s exact test and Mann-Whitney U test were used to investigate the association between dual-tracer PET findings, clinical and histopathological data. p-value significance was defined below the 0.05 level. Patients’ mean age was 70 years (range: 49–84) and mean PSA at time of PET/MR scans was 1.88 ng/mL (range: 0.21–14.4). A higher detection rate was observed for ⁶⁸Ga-PSMA PET/MRI, with more lesions being detected compared to ⁶⁸Ga-DOTA-RM2 PET/MRI (26/35 patients, 95 lesions vs. 15/31 patients, 41 lesions; p = 0.016 and 0.002). ⁶⁸Ga-PSMA and ⁶⁸Ga-DOTA-RM2 PET/MRI findings were discordant in 11/31 patients; among these, 10 were ⁶⁸Ga-PSMA positive (9/10 confirmed as true positive and 1/10 as false positive by follow-up examination). Patients with higher levels of PSA and shorter PSA doubling time (DT) presented more lesions on ⁶⁸Ga-PSMA PET/MRI (p = 0.006 and 0.044), while no association was found between PET findings and Gleason score. ⁶⁸Ga-PSMA has a higher detection rate than ⁶⁸Ga-DOTA-RM2 in detecting PCa recurrence. The number of ⁶⁸Ga-PSMA PET positive lesions is associated with higher levels of PSA and shorter PSA DT, thus representing potential prognostic factors.

Purpose: to investigate the preoperative role of ML-based classification using conventional ¹⁸F-FDG PET parameters and clinical data in predicting features of EC aggressiveness. Methods: retrospective study, including 123 EC patients who underwent ¹⁸F-FDG PET (2009–2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80–20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities. Results: on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression. Conclusions: ML-based classification using conventional ¹⁸F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.

Localized prostate cancer (PCa) can be treated with radical prostatectomy (RP). Up to 30% of patients undergoing this procedure experience biochemical recurrence (BCR), namely the rise in serum prostate-specific antigen (PSA) levels during the post-surgical follow-up, requiring further treatments and with the risk of severe disease progression. Currently, the most accurate imaging technique to confirm, detect, and locate disease relapses in BCR patients is prostate-specific membrane antigen (PSMA)-targeted PET, as recommended by international clinical guidelines. The aim of the study was to investigate potential clinical and pathological predictors of PSMA PET positivity, validated by clinical and instrumental follow-up or histopathological data. In this study, a selected cohort of BCR patients after RP and no other PCa-related therapy who underwent either PSMA PET/CT or PSMA PET/MRI has been analysed. Among the considered predictors, both pathological staging after RP equal or higher than pT3a and higher PSA levels at the time of the scan were significantly correlated with PSMA PET positivity on multivariate logistic regression analysis. As expected, PSMA PET confirmed its role as an accurate imaging technique in the setting of BCR in PCa. These findings may inform appropriate and tailored patient selection and scan timing to optimize and fully exploit this powerful diagnostic tool.

Background: PSMA PET radiomics is a promising tool for primary prostate cancer (PCa) characterisation. However, small single-centre studies and lack of external validation hinder definitive conclusions on the potential of PSMA PET radiomics in the initial workup of PCa. We aimed to validate a radiomics signature in a larger internal cohort and in an external cohort from a separate centre. Methods: One hundred and twenty-seven PCa patients were retrospectively enrolled across two independent hospitals. The first centre (IRCCS San Raffaele Scientific Institute, Centre 1) contributed 62 [⁶⁸Ga]Ga-PSMA-11 PET scans, 20 patients classified as low-grade (ISUP grade < 4), and 42 as high-grade (ISUP grade ≥ 4). The second centre (Stanford University Hospital, Centre 2) provided 65 [⁶⁸Ga]Ga-PSMA-11 PET scans, and 49 low-grade and 16 high-grade patients. A radiomics model previously generated in Centre 1 was tested on the two cohorts separately and afterward on the entire dataset. Then, we evaluated whether the radiomics features selected in the previous investigation could generalise to new data. Several machine learning (ML) models underwent training and testing using 100-fold Monte Carlo cross-validation, independently at both Centre 1 and Centre 2, with a 70–30% train–test split. Additionally, models were trained in one centre and tested in the other, and vice versa. Furthermore, data from both centres were combined for training and testing using Monte Carlo cross-validation. Finally, a new radiomics signature built on this bicentric dataset was proposed. Several performance metrics were computed. Results: The previously generated radiomics signature resulted in an area under the receiver operating characteristic curve (AUC) of 80.4% when tested on Centre 1, while it generalised poorly to Centre 2, where it reached an AUC of 62.7%. When the whole cohort was considered, AUC was 72.5%. Similarly, new ML models trained on the previously selected features yielded, at best, an AUC of 80.9% for Centre 1 and performed at chance for Centre 2 (AUC of 49.3%). A new signature built on this bicentric dataset reached, at best, an average AUC of 91.4% in the test set. Conclusions: The satisfying performance of radiomics models when used in the original development settings, paired with the poor performance otherwise observed, emphasises the need to consider centre-specific factors and dataset characteristics when developing radiomics models. Combining radiomics datasets is a viable strategy to reduce such centre-specific biases, but external validation is still needed.

This study proposed a new workflow for co-registering prostate PET images from a dual-tracer PET/MRI study with histopathological images of resected prostate specimens. The method aims to establish an accurate correspondence between PET/MRI findings and histology, facilitating a deeper understanding of PET tracer distribution and enabling advanced analyses like radiomics. To achieve this, images derived by three patients who underwent both [⁶⁸Ga]Ga-PSMA and [⁶⁸Ga]Ga-RM2 PET/MRI before radical prostatectomy were selected. After surgery, in the resected fresh specimens, fiducial markers visible on both histology and MR images were inserted. An ex vivo MRI of the prostate served as an intermediate step for co-registration between histological specimens and in vivo MRI examinations. The co-registration workflow involved five steps, ensuring alignment between histopathological images and PET/MRI data. The target registration error (TRE) was calculated to assess the precision of the co-registration. Furthermore, the DICE score was computed between the dominant intraprostatic tumor lesions delineated by the pathologist and the nuclear medicine physician. The TRE for the co-registration of histopathology and in vivo images was 1.59 mm, while the DICE score related to the site of increased intraprostatic uptake on [⁶⁸Ga]Ga-PSMA and [⁶⁸Ga]Ga-RM2 PET images was 0.54 and 0.75, respectively. This work shows an accurate co-registration method for histopathological and in vivo PET/MRI prostate examinations that allows the quantitative assessment of dual-tracer PET/MRI diagnostic accuracy at a millimetric scale. This approach may unveil radiotracer uptake mechanisms and identify new PET/MRI biomarkers, thus establishing the basis for precision medicine and future analyses, such as radiomics.