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Background: Our study aims to assess the feasibility and the reproducibility of fluorescent confocal microscopy (FCM) real-time assessment of urethral and ureteral margins during open radical cystectomy (ORC) for bladder cancer (BCa). Methods: From May 2020 to January 2022, 46 patients underwent ORC with intraoperative FCM evaluation. Each specimen was intraoperatively stained for histopathological analysis using FCM, analyzed as a frozen section (FSA), and sent for traditional H&E examination. Sensitivity, specificity, positive predictive value (PPV), and the negative predictive value (NPV) of FCM and FSA were assessed and compared with H&E for urethral and ureteral margins separately. Results: The agreement was evaluated through Cohen’s κ statistic. Urethral diagnostic agreement between FCM and FSA showed a κ = 0.776 (p < 0.001), while between FCM and H&E, the agreement was κ = 0.691 (p < 0.001). With regard to ureteral margins, an overall agreement of κ = 0.712 (p < 0.001) between FCM and FSA and of κ = 0.481 (p < 0.001) between FCM and H&E was found. Conclusions: FCM proved to be a safe, feasible, and reproducible method for the intraoperative assessment of urethral and ureteral margins during ORC. Compared to standard FSA, FCM showed adequate diagnostic performance in detecting urethral and ureteral malignant involvement.

Background: The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. Methods: From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation. Results: The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%. Conclusions: Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.