Traditional and Virtual Congress Meetings During the COVID-19 Pandemic and the Post-COVID-19 Era: Is it Time to Change the Paradigm?

The speed and reach of the COVID-19 pandemic have forced rapid changes in how we conduct medical practice and research. The rapid evolution in how scientific meetings are conducted may have long-term benefits. A new reality in which technology and sociality are merged may offer a more engaging and adaptable scientific congress experience with more flexible and dynamic use of content modulated to the needs of each attendee

Laparoscopic Nephron-Sparing Calycectomy for Treating Fraley's Syndrome

Background/Aims/Objectives: Various nephron-sparing approaches were described as part of surgical management for Fraley's syndrome, a rare anatomic variant of the renal vascular anatomy that compresses the upper pole infundibulum resulting in an upper calyceal obstruction and dilatation, with symptoms of flank pain and hematuria. To date, descriptions of minimally invasive correction techniques are anecdotal. METHODS: A retroperitoneal pure laparoscopic approach using the nephron-sparing technique was chosen in the presented case. RESULTS: In this report, we demonstrated that if laparoscopic calycectomy is performed without clamping of renal branches, parenchymal ischemia can be completely avoided. Additionally, the preservation of renal tissue surrounding the calyx enables the preservation of the intrasinusal segmental arteries flow, thereby avoiding their accidental closure by hemostatic sutures. CONCLUSIONS: In conclusion, Laparoscopic Nephron-Sparing Calycectomy is a safe and effective surgical procedure for the treatment of Fraley's syndrome. Consistent laparoscopic experience is required before embarking on this kind of surgery

Total Anatomical Reconstruction during Robot-assisted Radical Prostatectomy: Implications on Early Recovery of Urinary Continence

Background: The introduction of robotics revolutionized prostate cancer surgery because the magnified three-dimensional vision system and wristed instruments allow microsurgery to be performed. The advantages of robotic surgery could lead to improved continence outcomes in terms of early recovery compared with the traditional surgical methods. Objective: To describe the total anatomical reconstruction (TAR) technique during robot-assisted radical prostatectomy (RARP). Primary endpoint: evaluation of the continence rate at different time points. Secondary endpoint: evaluation of urine leakage and anastomosis stenosis rates related to the technique. Design, setting, and participants: June, 2013 to November, 2014; prospective consecutive series of patients with localized prostate cancer (cT1-3, cN0, cM0). Surgical procedure: RARP with TAR was performed in all cases. Lymph node dissection was performed if the risk of lymph nodal metastasis was over 5%, according to the Briganti updated nomogram. Measurements: Preoperative, intraoperative, postoperative, and pathological variables were analyzed. Enrolled patients were arbitrarily divided into three groups according to a time criterion. The relationships between the learning curve and the trend of the above-mentioned variables were analyzed using LOESS analysis. Continence was rigorously analyzed preoperatively and at 24h, 1 wk, 4 wk, 12 wk, and 24 wk after catheter removal. Results and limitations: In total, 252 patients were analyzed. The continence rates immediately after catheter removal and at 1 wk, 4 wk, 12 wk, and 24 wk after RARP were 71.8%, 77.8%, 89.3%, 94.4%, and 98.0%, respectively. Multivariate analysis revealed that the nerve sparing technique, D'Amico risk groups, lymph node dissection, and prostate volume were involved in the early recovery of urinary continence. One ileal perforation requiring reoperation was recorded. The transfusion rate was 0.8%. Thirty-one (12.3%) postoperative complications were recorded up to 6 mo after surgery. Among these, eight acute urinary retentions (3.2%) and three urine leakages (1.2%) were recorded. There was a lack of randomization and comparison with other techniques. Both anatomical dissection of the prostatic apex and TAR were used. The results may not be generalized to low-volume centers. Conclusions: The TAR technique showed promising results in the early recovery of urinary continence, as well as watertight anastomosis and a low rate of urine leakage. The oncologic results were not affected. Comparative studies are needed to support the quality of reported results

Artificial intelligence for target prostate biopsy outcomes prediction the potential application of fuzzy logic

Background: In current precision prostate cancer (PCa) surgery era the identification of the best patients candidate for prostate biopsy still remains an open issue. The aim of this study was to evaluate if the prostate target biopsy (TB) outcomes could be predicted by using artificial intelligence approach based on a set of clinical pre-biopsy. Methods: Pre-biopsy characteristics in terms of PSA, PSA density, digital rectal examination (DRE), previous prostate biopsies, number of suspicious lesions at mp-MRI, lesion volume, lesion location, and Pi-Rads score were extracted from our prospectively maintained TB database from March 2014 to December 2019. Our approach is based on Fuzzy logic and associative rules mining, with the aim to predict TB outcomes. Results: A total of 1448 patients were included. Using the Frequent-Pattern growth algorithm we extracted 875 rules and used to build the fuzzy classifier. 963 subjects were classified whereas for the remaining 484 subjects were not classified since no rules matched with their input variables. Analyzing the classified subjects we obtained a specificity of 59.2% and sensitivity of 90.8% with a negative and the positive predictive values of 81.3% and 76.6%, respectively. In particular, focusing on ISUP ≥ 3 PCa, our model is able to correctly predict the biopsy outcomes in 98.1% of the cases. Conclusions: In this study we demonstrated that the possibility to look at several pre-biopsy variables simultaneously with artificial intelligence algorithms can improve the prediction of TB outcomes, outclassing the performance of PSA, its derivates and MRI alone

New multiport robotic surgical systems: a comprehensive literature review of clinical outcomes in urology

Over the past 20 years, the field of robotic surgery has largely been dominated by the da Vinci robotic platform. Nevertheless, numerous novel multiport robotic surgical systems have been developed over the past decade, and some have recently been introduced into clinical practice. This nonsystematic review aims to describe novel surgical robotic systems, their individual designs, and their reported uses and clinical outcomes within the field of urologic surgery. Specifically, we performed a comprehensive review of the literature regarding the use of the Senhance robotic system, the CMR-Versius robotic system, and the Hugo RAS in urologic procedures. Systems with fewer published uses are also described, including the Avatera, Hintori, and Dexter. Notable features of each system are compared, with a particular emphasis on factors differentiating each system from the da Vinci robotic system

Machine-Learning-Based Tool to Predict Target Prostate Biopsy Outcomes: An Internal Validation Study

Abstract: The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases