Purpose: To define an imaging risk profile in a population of patients affected by Pancreatic neuroendocrine neoplasms (PanNENs) candidates to surgery, by assessing the predictive role of 68Ga-DOTATOC and 18F-FDG PET/CT and PET/MR derived parameters in risk stratification, particularly regarding histological features of aggressive behaviour. Patients and methods: Retrospective study including 83 patients (53 males, 30 females; median age: 60 years, interquartile range 52–66.5), who underwent to 68Ga-DOTATOC (PET/CT: n = 77; PET/MR: n = 6) and, 68/83 patients, also to 18F-FDG PET (PET/CT: n = 65; PET/MR: n = 3) before surgery for PanNEN between 2011 and 2019, with available histological and follow-up data. The PET scans were interpreted with both qualitative (positive vs. negative) and semiquantitative measurements as follows: maximum and mean standardized uptake value (SUVmax and SUVmean) for both 18F-FDG and 68Ga-DOTATOC scans, metabolic tumour volume (MTV) and tumour lesion glycolysis (TLG) for 18F-FDG scans and somatostatin receptor density (SRD) and total lesion somatostatin receptor density (TLSRD) for 68Ga-DOTATOC PET. Receiver Operating Characteristics (ROC) curve analysis was used to investigate the performance of several PET parameters in predicting tumour stage or characteristic. For each PET parameter, the optimal cut-off was derived. Logistic regression analysis was used to assess if the PET parameters, categorized with the optimal cut-off values, were able to predict significantly the corresponding tumour stage or characteristic. Results: Overall, 29 (35%) patients had G1, 49 (59%) a G2 and five (6%) had a G3 PanNEN. The median Ki-67 index was 4% (interquartile range: 1–8%). SRD and TLSRD significantly discriminated between pT3 or pT4 PanNEN versus pT1 or pT2, as well as 18F-FDG MTV and TLG. 68Ga-DOTATOC SUVmax was able to significantly predict the presence of distant metastases with a threshold of 51.27 (sensitivity and specificity of 85.7 and 68.1%, respectively). 18F-FDG MTV and TLG were predictors of angioinvasion. The cut-off threshold for MTV was 7.98 (sensitivity and specificity of 69.7 and 82.4%, respectively) (p = 0.0004) whereas the cut-off for TLG was 32.4 (sensitivity and specificity of 69.7% and 82.4%, respectively) (p = 0.0004). Conclusion: Dual tracer 68Ga-DOTATOC and 18F-FDG PET scans provide relevant information regarding tumour behaviour and aggressiveness, implementing the diagnostic preoperative work-up.

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.