External validation of a nomogram for outcome prediction in management of medium-sized (1-2 cm) kidney stones.

BACKGROUND: Stone nomogram by Micali et al., able topredict treatment failure of shock-wave lithotripsy (SWL), retrograde intrarenal surgery (RIRS) and percutaneous nephrolithotomy (PNL) in the management of single 1-2 cm renal stones, was developed on 2605 patients and showed a high predictive accuracy, with an area under ROC curve of 0.793 at internal validation. The aim of the present study is to externally validate the model to assess whether it displayed a satisfactory predictive performance if applied to different populations. METHODS: External validation was retrospectively performed on 3025 patients who underwent an active stone treatment from December 2010 to June 2021 in 26 centers from four countries (Italy, USA, Spain, Argentina). Collected variables included: age, gender, previous renal surgery, preoperative urine culture, hydronephrosis, stone side, site, density, skin-to-stone distance. Treatment failure was the defined outcome (residual fragments >4 mm at three months CT-scan). RESULTS: Model discrimination in external validation datasets showed an area under ROC curve of 0.66 (95% 0.59-0.68) with adequate calibration. The retrospective fashion of the study and the lack of generalizability of the tool towards populations from Asia, Africa or Oceania represent limitations of the current analysis. CONCLUSIONS: According to the current findings, Micali's nomogram can be used for treatment prediction after SWL, RIRS and PNL; however, a lower discrimination performance than the one at internal validation should be acknowledged, reflecting geographical, temporal and domain limitation of external validation studies. Further prospective evaluation is required to refine and improve the nomogram findings and to validate its clinical value.

Current applications of ex-vivo fluorescent confocal microscope in urological practice: a systematic review of literature.

BACKGROUND: Histopathological examination, a cornerstone in diagnosing cancer, faces challenges due to its time-consuming nature. This review explores the potential of ex-vivo fluorescent confocal microscopy (FCM) in urology, addressing the need for real-time pathological assessment, particularly in prostate cancer. This systematic review aims to assess the applications of FCM in urology, including its role in prostate cancer diagnosis, surgical margin assessment, and other urological fields. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, a systematic search of PubMed and SCOPUS was conducted, focusing on English written original articles published after January 1, 2018, discussing the use of FCM in urological practice. The search included keywords related to FCM and urological terms. The risk of bias assessment was performed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: A total of 17 relevant studies were included in the review that focuses on three main urological issues: prostate cancer (15 articles), bladder cancer (1 article), and renal biopsy (1 article). FCM exhibited significant promise in diagnosing prostate cancer. These studies reported an accuracy range of 85.33% to 95.1% in distinguishing between cancerous and non-cancerous prostate tissues. Moreover, FCM proved valuable for assessing surgical margins in real-time during radical prostatectomy, reducing the need for frozen section analysis. In some investigations, researchers explored the integration of artificial intelligence (AI) with FCM to automate diagnostic processes. Concerning bladder cancer, FCM played a beneficial role in evaluating urethral and ureteral margins during radical cystectomy. Notably, it showed substantial agreement with conventional histopathology and frozen section examination. In the context of renal biopsy, FCM demonstrated the potential to differentiate normal renal parenchyma from cancerous tissue, although the available evidence is limited in this area. The main limitation of the current study is the scarcity of data regarding the topic of interest. CONCLUSIONS: Ex-vivo FCM holds promise in urology, particularly in prostate cancer diagnosis and surgical margin assessment. Its real-time capabilities may reduce diagnostic delays and patient stress. However, most studies remain experimental, requiring further research to validate clinical utility.

Stone centers: a national survey on surgical techniques performed in Italy.

BACKGROUND: Recurrent and complex stone disease may be considered a challenging disease. In 2018, the Calculus group of the SIU (Italian Society of Urology) set itself the goal of establishing the minimum requirements for a center that could continuously manage urolithiasis pathology, named a Stone Center. In this study we present the results of a pilot survey carried out in 2019 with the aim of drawing a map of the situation of Italian urological centers dealing with urinary stones. METHODS: A total of 260 national urology departments dealing with urolithiasis surgery were contacted for this study. A survey was issued to each of the centers to determine the number of patients treated for urinary stones and the amount of procedures performed per year: 1) extracorporeal shock wave lithotripsy ESWL; 2) ureterorenoscopy URS; 3) retrograde intrarenal surgery RIRS; 4) percutaneous nephrolithotomy PCNL. RESULTS: Out of 260 centers contacted, 188 fulfilled the survey. Outcomes were quite variable, with approximately 37% of the centers lacking a lithotripter, and 46% of those that did have it performing fewer than 100 treatments per year. In terms of endoscopic procedures, more than 80% of the centers contacted performed URS or RIRS; however, when it came to percutaneous lithotripsy, these numbers dropped significantly; 33% of the centers contacted did not perform PCNL, and of those who did, 18% had less than 5 years of experience as a center. CONCLUSIONS: Our survey shows a very heterogeneous national picture about urolithiasis treatments. Our goal is to create national paradigms to be able to define stone centers where the patient suffering from complex urinary stones can find a network of professionals with an adequate armamentarium suitable for the management of their pathology.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature.

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.