Treatment of renal stones by extracorporeal shockwave lithotripsy - An update

Aim: Despite the extensive experience with minimal invasive stone therapy, there are still different views on the ideal management of renal stones. Materials and Methods: Analysis of the literature includes more than 14,000 patients. We have compared these data with long-term results of two major stone centers in Germany. The results have been compared concerning the anatomical kidney situation, stone size, stone localization and observation time. Results: According to the importance of residual fragments following extracorporeal shock wave lithotripsy (ESWL), we have to distinguish between clinically insignificant residual fragments and clinically significant residual fragments (CIRF). 24 months following ESWL stone passage occurs as a continous process, and if there are no clinical symptoms, any endoscopic procedure should be considered as overtreatment. According to these results, stone-free rates of patients increase in longer follow-up periods. Newer ESWL technology has increased the percentage of CIRF. Conclusion: We consider ESWL in most patients with renal calculi as first-line treatment, except in patients with renal calculi bigger than 30 mm in diameter. Copyright (C) 2001 S. Karger AG, Basel

Laparoscopic Radical Nephrectomy: The New Gold Standard Surgical Treatment for Localized Renal Cell Carcinoma

We will try to demonstrate that laparoscopic radical nephrectomy could be the new gold standard treatment for renal cell carcinoma with the aid of the current reports exploring the advantages and disadvantages of laparoscopic radical nephrectom overopen surgery

Approach to endoscopic extraperitoneal radical prostatectomy (EERPE): the impact of previous laparoscopic experience on the learning curve

BACKGROUND: We report our approach regarding the technique of endoscopic extraperitoneal radical prostatectomy (EERPE) and analyze the learning curve of two surgeons after thorough technical training under expert monitoring. The purpose of this study was to investigate the influence of expert monitoring on the surgical outcome and whether previous laparoscopic experience influences the surgeon's learning curve. METHODS: EERPE was performed on 120 consecutive patients by two surgeons with different experience in laparoscopy. An analysis and comparison of their learning curve was made. RESULTS: Median operation time: 200 (110-415) minutes. Complications: no conversion, blood transfusion (1.7%), rectal injury (3.3%). Median catheterisation time: 6 (5-45) days. Histopathological data: 55% pT2, 45% pT3 with a positive surgical margin rate of 6.1% and 46%, respectively. After 12 months, 78% of the patients were continent, 22% used 1 or more pad. Potency rate with or without PDE-5-inhibitors was 66% with bilateral and 31% with unilateral nerve-sparing, respectively. Operation time was the only parameter to differ significantly between the two surgeons. CONCLUSION: EERPE can be learned within a short teaching phase. Previous laparoscopic experience is reflected by shorter operation times, not by lower complication rates or superior early oncological data

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention