Preoperative assessment of lymph nodal metastases with [68Ga]Ga-DOTATOC PET radiomics for improved surgical planning in well-differentiated pancreatic neuroendocrine tumours.

PURPOSE: Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [68Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention. METHODS: Seventy-two patients who performed preoperative [68Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [68Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney). RESULTS: LN metastases were detected in 29/72 patients at histology. [68Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*). CONCLUSION: In this study, [68Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Diagnostic accuracy of fully hybrid [68Ga]Ga-PSMA-11 PET/MRI and [68Ga]Ga-RM2 PET/MRI in patients with biochemically recurrent prostate cancer: a prospective single-center phase II clinical trial.

PURPOSE: To compare the diagnostic accuracy and detection rates of PET/MRI with [68Ga]Ga-PSMA-11 and [68Ga]Ga-M2 in patients with biochemical recurrence of prostate cancer (PCa). METHODS: Sixty patients were enrolled in this prospective single-center phase II clinical trial from June 2020 to October 2022. Forty-four/60 completed all study examinations and were available at follow-up (median: 22.8 months, range: 6-31.5 months). Two nuclear medicine physicians analyzed PET images and two radiologists interpreted MRI; images were then re-examined to produce an integrated PET/MRI report for both [68Ga]Ga-PSMA-11 and [68Ga]Ga-RM2 examinations. A composite reference standard including histological specimens, response to treatment, and conventional imaging gathered during follow-up was used to validate imaging findings. Detection rates, accuracy, sensitivity, specificity, positive, and negative predictive value were assessed. McNemar's test was used to compare sensitivity and specificity on a per-patient base and detection rate on a per-region base. Prostate bed, locoregional lymph nodes, non-skeletal distant metastases, and bone metastases were considered. p-value significance was defined below the 0.05 level after correction for multiple testing. RESULTS: Patients' median age was 69.8 years (interquartile range (IQR): 61.8-75.1) and median PSA level at time of imaging was 0.53 ng/mL (IQR: 0.33-2.04). During follow-up, evidence of recurrence was observed in 31/44 patients. Combining MRI with [68Ga]Ga-PSMA-11 PET and [68Ga]Ga-RM2 PET resulted in sensitivity = 100% and 93.5% and specificity of 69.2% and 69.2%, respectively. When considering the individual imaging modalities, [68Ga]Ga-RM2 PET showed lower sensitivity compared to [68Ga]Ga-PSMA-11 PET and MRI (61.3% vs 83.9% and 87.1%, p = 0.046 and 0.043, respectively), while specificity was comparable among the imaging modalities (100% vs 84.6% and 69.2%, p = 0.479 and 0.134, respectively). CONCLUSION: This study brings further evidence on the utility of fully hybrid PET/MRI for disease characterization in patients with biochemically recurrent PCa. Imaging with [68Ga]Ga-PSMA-11 PET showed high sensitivity, while the utility of [68Ga]Ga-RM2 PET in absence of a simultaneous whole-body/multiparametric MRI remains to be determined.

The potential role of osteoporosis in unspecific [18F]PSMA-1007 bone uptake.

AIM: Unspecific bone uptake is one of the main limitations of PET imaging with some PSMA-targeting radiopharmaceuticals, especially with [18F]PSMA-1007. We explored the potential association between osteoporosis and the occurrence of unspecific [18F]PSMA-1007 bone uptake investigating markers which might correlate with bone mineral density. MATERIALS AND METHODS: We retrospectively analyzed treatment-naïve patients with a confirmed diagnosis of prostate adenocarcinoma who underwent staging [18F]PSMA-1007 positron emission tomography (PET). Qualitative image analysis was performed independently by three experienced nuclear medicine physicians. Patients were divided in two groups according to the presence/absence of unspecific bone uptake. Clinical information, blood count parameters (assessed within 3 months to the PET scan), body mass index (BMI), and bone density as estimated by computed tomography were collected. The Kruskal-Wallis and t-test were used to compare parameters. RESULTS: We analyzed 77 patients: 29 of them (38%) had unspecific bone uptake at [18F]PSMA-1007 PET, most commonly in the pelvic bones (69%) and ribs (62%). We did not find any significant difference in clinical parameters in the two groups. In patients with unspecific bone uptake, white blood cell, and neutrophil counts were significantly higher; in the same group, we observed lower values of BMI and bone density, although not statistically different. CONCLUSIONS: We observed unspecific bone uptake on [18F]PSMA-1007 PET in more than 1/3 of patients. In this exploratory analysis, we found a significant correlation between blood count parameters and unspecific [18F]PSMA-1007 bone uptake. We may speculate that [18F]PSMA-1007 unspecific bone uptake could be associated with osteoporosis. This hypothesis needs to be further investigated in larger populations and exploring more specific markers of osteoporosis.

Systematic Review and Metanalysis on the Role of Prostate-Specific Membrane Antigen Positron Emission Tomography/Magnetic Resonance Imaging for Intraprostatic Tumour Assessment.

The present systematic review and meta-analysis are focused on the diagnostic accuracy of PSMA PET/MRI in primary prostate cancer assessment. A literature search was conducted on the PubMed database using the terms "PSMA" AND "prostate cancer" or "prostate" AND "PET/MRI" or "PET MRI" or "PET-MRI" or "PET-MR" AND "primary" or "staging." Ten articles were eligible for analysis after applying the exclusion criteria. PET/MRI showed better diagnostic accuracy in detecting primary PCa compared to multiparametric (mp) MRI and PET alone. The pooled sensitivity and specificity of 68Ga-PSMA PET/MRI at the per-patient level were 0.976 (CI: 0.943-0.991) and 0.739 (CI: 0.437-0.912); respectively. PSMA PET/MRI has good sensitivity in detecting primary PCa, especially in patients with PIRADS 3 PCa.

Predictors of PSMA PET Positivity: Analysis in a Selected Cohort of Biochemical Recurrence Prostate Cancer Patients after Radical Prostatectomy.

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.

[68Ga]Ga-PSMA and [68Ga]Ga-RM2 PET/MRI vs. Histopathological Images in Prostate Cancer: A New Workflow for Spatial Co-Registration.

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.

Somatostatin receptor activity assessed by 68Ga-DOTATOC PET can preoperatively predict DAXX/ATRX loss of expression in well-differentiated pancreatic neuroendocrine tumors.

PURPOSE: To evaluate the role of 68Ga-DOTATOC PET parameters in predicting DAXX/ATRX loss of expression in patients with Pancreatic neuroendocrine tumors (PanNET) candidate to surgery. METHODS: This retrospective study included 72 consecutive patients with PanNET (January 2018-March 2022) who underwent to 68Ga-DOTATOC PET for preoperative staging. Image analysis: qualitative assessment and extraction of SUVmax, SUV mean, somatostatin receptor density (SRD), and total lesion somatostatin receptor density (TLSRD) from primary PanNET. Radiological diameter and biopsy information (grade, Ki67) were collected. Loss of expression (LoE) of DAXX/ATRX was assessed by immunohistochemistry on surgical specimen. Student t-test, univariate and multivariate logistic regression and ROC curves have been used to investigate the predictive value of PET parameters on DAXX/ATRX LoE. RESULTS: Forty-two/72 patients had a G1, 28/72 a G2, and 2/72 a G3 PanNET. Seven/72 patients had DAXX LoE, 10/72 ATRX LoE, and 2/72 DAXX/ATRX LoE. SRD and TLSRD could predict DAXX LoE (p = 0.002, p = 0.018, respectively). By evaluating SRD in combination with radiological diameter, only SRD maintained statistical significance (multivariate logistic regression: p = 0.020, OR = 1.05), providing the best prediction (AUC-ROC = 79.01%; cut-off = 46.96; sensitivity = 77.78%; specificity = 88.89%). In the sub-analysis performed on 55 patients with biopsy availability, SRD demonstrated its role in providing useful and additional information (multivariate logistic regression: SRD p = 0.007; grade p = 0.040). CONCLUSION: SRD has a predictive role on DAXX LoE in PanNETs, with higher probability of LoE at increasing SRD values. SRD provides complementary/additional information to grade assessed on biopsy material, and the combined use of these approaches might support patients' management by preoperatively identifying subjects with more aggressive diseases.

External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images.

Introduction: State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images. Methods: Eighty-five biopsy proven prostate cancer patients who underwent 68Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work. Results: When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring). Discussion: In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.

Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer.

PURPOSE: The aim of this study is to investigate the role of [68Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (PSISUP) grade in primary prostate cancer (PCa). METHODS: This retrospective study included 47 PCa patients who underwent [68Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of PSISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal-Wallis and Mann-Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models' performance. The predictions of the best performing model were compared against ISUP grade at biopsy. RESULTS: ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM-Zone Entropy and Shape-Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann-Whitney p > 0.05). CONCLUSION: These findings support the role of [68Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PSISUP grade.

Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness.

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.

18F-FDG PET/CT May Predict Tumor Type and Risk Score in Gestational Trophoblastic Disease.

PURPOSE: The aim of this study was to investigate the role of 18F-FDG PET/CT in predicting pathological prognostic factors, including tumor type and International Federation of Gynecology and Obstetrics (FIGO) score, in gestational trophoblastic disease (GTD). METHODS: Retrospective monocentric study including 24 consecutive patients who underwent to 18F-FDG PET/CT from May 2005 to March 2021 for GTD staging purpose. The following semiquantitative PET parameters were measured from the primary tumor and used for the analysis: maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV) and total lesion glycolisis (TLG). Statistical analysis included Spearman correlation coefficient to evaluate the correlations between imaging parameters and tumor type (nonmolar trophoblastic vs postmolar trophoblastic tumors) and risk groups (high vs low, defined according to the FIGO score), whereas area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the predictive value of the PET parameters. Mann-Whitney U test was used to further describe the parameter's potential in differentiating the populations. RESULTS: SUVmax and SUVmean resulted fair (AUC, 0.783; 95% confidence interval [CI], 0.56-0.95) and good (AUC, 0.811; 95% CI, 0.59-0.97) predictors of tumor type, respectively, showing a low (ρ = 0.489, adjusted P = 0.030) and moderate (ρ = 0.538, adjusted P = 0.027) correlation. According to FIGO score, TLG was instead a fair predictor (AUC, 0.770; 95% CI, 0.50-0.99) for patient risk stratification. CONCLUSIONS: 18F-FDG PET parameters have a role in predicting GTD pathological prognostic factors, with SUVmax and SUVmean being predictive for tumor type and TLG for risk stratification.

68Ga-PSMA and 68Ga-DOTA-RM2 PET/MRI in Recurrent Prostate Cancer: Diagnostic Performance and Association with Clinical and Histopathological Data.

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.

Functional connectivity in multiple sclerosis modelled as connectome stability: A 5-year follow-up study.

BACKGROUND: Brain functional connectivity (FC) in multiple sclerosis (MS) is abnormal compared to healthy controls (HCs). More longitudinal studies in MS are needed to evaluate whether FC stability is clinically relevant. OBJECTIVE: To compare functional magnetic resonance imaging (fMRI)-based FC between MS and HC, and to determine the relationship between longitudinal FC changes and structural brain damage, cognitive performance and physical disability. METHODS: T1-weighted MPRAGE and resting-state fMRI (1.5T) were acquired from 70 relapsing-remitting MS patients and 94 matched HC at baseline (mean months since diagnosis 14.0 ± 11) and from 60 MS patients after 5 years. Independent component analysis and network modelling were used to measure longitudinal FC stability and cross-sectional comparisons with HC. Linear mixed models, adjusted for age and sex, were used to calculate correlations. RESULTS: At baseline, patients with MS showed FC abnormalities both within networks and in single connections compared to HC. Longitudinal analyses revealed functional stability and no significant relationships with clinical disability, cognitive performance, lesion or brain volume. CONCLUSION: FC abnormalities occur already at the first decade of MS, yet we found no relevant clinical correlations for these network deviations. Future large-scale longitudinal fMRI studies across a range of MS subtypes and outcomes are required.

State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review.

We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.

Preliminary Results of an Ongoing Prospective Clinical Trial on the Use of 68Ga-PSMA and 68Ga-DOTA-RM2 PET/MRI in Staging of High-Risk Prostate Cancer Patients.

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.