The Vesical Imaging-Reporting and Data System (VI-RADS) has been introduced to provide preoperative bladder cancer staging and has proved to be reliable in assessing the presence of muscle invasion in the pre-TURBT (trans-urethral resection of bladder tumor). We aimed to assess through a systematic review and meta-analysis the inter-reader variability of VI-RADS criteria for discriminating non-muscle vs. muscle invasive bladder cancer (NMIBC, MIBC). PubMed, Web of Science, Cochrane, and Embase were searched up until 30 July 2020. The Quality Appraisal of Diagnostic Reliability (QAREL) checklist was utilized to assess the quality of included studies and a pooled measure of inter-rater reliability (Cohen’s Kappa [κ] and/or Intraclass correlation coefficients (ICCs)) was calculated. Further sensitivity analysis, subgroup analysis, and meta-regression were conducted to investigate the contribution of moderators to heterogeneity. In total, eight studies between 2018 and 2020, which evaluated a total of 1016 patients via 21 interpreting genitourinary (GU) radiologists, met inclusion criteria and were critically examined. No study was considered to be significantly flawed with publication bias. The pooled weighted mean κ estimate was 0.83 (95%CI: 0.78–0.88). Heterogeneity was present among the studies (Q = 185.92, d.f. = 7, p < 0.001; I2 = 92.7%). Meta-regression analyses showed that the relative % of MIBC diagnosis and cumulative reader’s experience to influence the estimated outcome (Coeff: 0.019, SE: 0.007; p= 0.003 and 0.036, SE: 0.009; p = 0.001). In the present study, we confirm excellent pooled inter-reader agreement of VI-RADS to discriminate NMIBC from MIBC underlying the importance that standardization and reproducibility of VI-RADS may confer to multiparametric magnetic resonance (mpMRI) for preoperative BCa staging.

To determine Vesical Imaging-Reporting and Data System (VI-RADS) score 5 accuracy in predicting locally advanced bladder cancer (BCa), so as to potentially identify those patients who could avoid the morbidity of deep transurethral resection of bladder tumour (TURBT) in favour of histological sampling-TUR prior to radical cystectomy (RC). (II) To explore the predictive value of VI-RADS score 5 on time-to-cystectomy (TTC) outcomes. Patients and Methods We retrospectively reviewed patients' ineligible or refusing cisplatin-based combination neoadjuvant chemotherapy who underwent multiparametric magnetic resonance imaging (mpMRI) of the bladder prior to staging TURBT followed by RC for muscle-invasive BCa. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) were calculated for VI-RADS score 5 vs. score 2-4 cases to assess the accuracy of mpMRI for extravesical BCa detection (≥pT3). VI-RADS score performance was assessed by receiver operating characteristics curve analysis. A Κ statistic was calculated to estimate mpMRI and pathological diagnostic agreement. The risk of delayed TTC (i.e. time from initial BCa diagnosis of >3 months) was assessed using multivariable logistic regression model. Results A total of 149 T2-T4a, cN0-M0 patients (VI-RADS score 5, n = 39 vs VI-RADS score 2-4, n = 110) were examined. VI-RADS score 5 demonstrated sensitivity, specificity, PPV and NPV, in detecting extravesical disease of 90.2% (95% confidence interval [CI] 84-94.3), 98.1% (95% CI 94-99.6), 94.9% (95% CI 89.6-97.6) and 96.4% (95% CI 91.6-98.6), respectively. The area under the curve was 94.2% (95% CI 88.7-99.7) and inter-reader agreement was excellent (Κ inter 0.89). The mean (SD) TTC was 4.2 (2.3) and 2.8 (1.1) months for score 5 vs 2-4, respectively (P < 0.001). VI-RADS score 5 was found to independently increase risk of delayed TTC (odds ratio 2.81, 95% CI 1.20-6.62). Conclusion The VI-RADS is valid and reliable in differentiating patients with extravesical disease from those with muscle-confined BCa before TURBT. Detection of VI-RADS score 5 was found to predict significant delay in TTC independently from other clinicopathological features. In the future, higher VI-RADS scores could potentially avoid the morbidity of extensive primary resections in favour of sampling-TUR for histology. Further prospective, larger, and multi-institutional trials are required to validate clinical applicability of our findings.

Background Prostate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality. Purpose To develop a convolutional neural networks (CNNs) based analysis pipeline for the classification of prostate MRI image quality. Study Type Retrospective. Subjects Three hundred sixteen prostate mpMRI scans and 312 men (median age 67). Field Strength/Sequence A 3 T; fast spin echo T2WI, echo planar imaging DWI, ADC, gradient‐echo dynamic contrast enhanced (DCE). Assessment MRI scans were reviewed by three genitourinary radiologists (V.P., M.D.M., S.C.) with 21, 12, and 5 years of experience, respectively. Sequences were labeled as high quality (Q1) or low quality (Q0) and used as the reference standard for all analyses. Statistical Tests Sequences were split into training, validation, and testing sets (869, 250, and 120 sequences, respectively). Inter‐reader agreement was assessed with the Fleiss kappa. Following preprocessing and data augmentation, 28 CNNs were trained on MRI slices for each sequence. Model performance was assessed on both a per‐slice and a per‐sequence basis. A pairwise t ‐test was performed to compare performances of the classifiers. Results The number of sequences labeled as Q0 or Q1 was 38 vs. 278 for T2WI, 43 vs. 273 for DWI, 41 vs. 275 for ADC, and 38 vs. 253 for DCE. Inter‐reader agreement was almost perfect for T2WI and DCE and substantial for DWI and ADC. On the per‐slice analysis, accuracy was 89.95% ± 0.02% for T2WI, 79.83% ± 0.04% for DWI, 76.64% ± 0.04% for ADC, 96.62% ± 0.01% for DCE. On the per‐sequence analysis, accuracy was 100% ± 0.00% for T2WI, DWI, and DCE, and 92.31% ± 0.00% for ADC. The three best algorithms performed significantly better than the remaining ones on every sequence ( P ‐value < 0.05). Data Conclusion CNNs achieved high accuracy in classifying prostate MRI image quality on an individual‐slice basis and almost perfect accuracy when classifying the entire sequences. Evidence Level 4 Technical Efficacy Stage 1

Background: Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with suspicion of prostate cancer (PCa). The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly costeffective PCa diagnostic workup.Objective: To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. Materials and Methods:Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers.Results: Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies comparing non-contrast with contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra-and extra-prostatic extension and regional disease. The mentioned observations suggest that low-quality mpMRI for the general population might represent the main shortage to overcome.Discussion: Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic; bpMRI has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. Conclusion:Today, the accessibility to MRI for early detection of PCa is a priority.Results from prospective, multicenter, multireader, and paired validation studies are needed to provide evidence supporting its role in the clinical practice.