Introduction: specialists have an urge for biomarkers that can discriminate indolent prostate cancer from aggressive tumors. Ki67 is a proliferation marker, and its expression is associated with the aggressiveness of several cancers. Objective: analyze the expression of Ki67 in prostate cancer samples correlating with the aggressiveness of the disease. Methods: Ki67 mRNA levels were determined utilizing data from a TCGA cohort (Tumor(n)=492 and control(n)=52). The protein expression was determined on 94 biopsies from patients by immunohistochemical assay. Results: in mRNA, the Ki67 upregulation is associated with cancer tissue (p<0.0001) and worst disease-free survival (p=0.035). The protein upregulation is associated with increase of the ISUP score (p<0.0001), cancer stage (p=0.05), biochemical recurrence (p=0.0006) and metastasis (p<0.0001). We also show a positive correlation between Ki67 expression and ISUP score (r=0.5112, p<0.0001) and disease risk stratification (r=0.3388, p=0.0009). Ki67 expression is a factor independently associated with biochemical recurrence (p=0.002) and metastasis (p<0.0001). Finally, the patients with high Ki67expression shows better survival regarding biochemical recurrence (p=0.008) and metastasis (p=0.056). Patients with high Ki67 expression are 2.62 times more likely to develop biochemical recurrence (p=0.036). Conclusion: Ki67 upregulation is associated with prostate cancer aggressiveness.

Introduction: despite being infrequent, urinary incontinence has a huge impact on the quality of life of patients undergoing radical prostatectomy, even with the robotic-assisted technique. Objective: to assess the evolution of urinary symptoms from preoperative to 12 months after robotic-assisted radical prostatectomy. Methods: data was collected from 998 patients who underwent robotic-assisted radical prostatectomy. Demographic data, preoperative and postoperative information on patients were documented. The ICIQ and IPSS questionnaires were also applied preoperatively and after 1, 3, 6 and 12 months after the operation. Results: Out of 998 patients, 257 correctly completed all questionnaires. The mean age of the patients was 60 ± 0.74 years. We found that the total IPSS increased initially and at 6 months after the operation, it was already lower than the initial preoperative value (7.76 at 6 months vs. 9.90 preoperative, p <0.001), being that questions regarding voiding symptoms were the first to improve followed by the questions regarding post micturition and storage symptoms. As for the ICIQ variables, there was an increase with radical prostatectomy and none of them returned to the preoperative level (p<0.001). Conclusions: robotic assisted radical prostatectomy causes, at first, a worsening of urinary symptoms in the lower tract with subsequent recovery. Recovery begins with voiding symptoms, followed by post micturition and storage symptoms. The symptoms assessed by the IPSS evolve to better parameters even than those of the preoperative period, while the symptoms of incontinence assessed by the ICIQ do not reach the preoperative levels in the studied interval.

Introduction: flexible ureteroscopy is a minimally invasive surgical technique used for the treatment of renal lithiasis. Postoperative urosepsis is a rare but potentially fatal complication. Traditional models used to predict the risk of this condition have limited accuracy, while models based on artificial intelligence are more promising. The objective of this study is to carry out a systematic review regarding the use of artificial intelligence to detect the risk of sepsis in patients with renal lithiasis undergoing flexible ureteroscopy. Methods: the literature review is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The keyword search was performed in MEDLINE, Embase, Web of Science and Scopus and resulted in a total of 2,496 articles, of which 2 met the inclusion criteria. Results: both studies used artificial intelligence models to predict the risk of sepsis after flexible uteroscopy. The first had a sample of 114 patients and was based on clinical and laboratory parameters. The second had an initial sample of 132 patients and was based on preoperative computed tomography images. Both obtained good measurements of Area Under the Curve (AUC), sensitivity and specificity, demonstrating good performance. Conclusion: artificial intelligence provides multiple effective strategies for sepsis risk stratification in patients undergoing urological procedures for renal lithiasis, although further studies are needed.