Background: The Retzius space-sparing robot-assisted radical prostatectomy (RS-RARP) has shown better results in urinary continence, but its efficacy and safety compared to conventional robot-assisted radical prostatectomy (c-RARP) remain controversial.Material and Methods: A research was conducted in Medline via PubMed, Cochrane Library, EMBASE, and Web of Science up to January 4, 2021, to identify studies comparing RS-RARP to c-RARP. We used RevMan 5.3 and STATA 14.0 for meta-analysis.Results: A total of 14 studies involving 3,129 participants were included. Meta-analysis showed no significant difference in positive surgical margins (PSMs), but the RS-RARP group had significantly higher PSM rates in the anterior site [odds ratio (OR) = 2.25, 95% CI: 1.22–4.16, P = 0.01]. Postoperative continence in RS-RARP group at 1 month (OR = 5.72, 95% CI: 3.56–9.19, P < 0.01), 3 months (OR = 6.44, 95% CI: 4.50–9.22, P < 0.01), 6 months (OR = 8.68, 95% CI: 4.01–18.82, P < 0.01), and 12 months (OR = 2.37, 95% CI: 1.20–4.70, P = 0.01) was significantly better than that in the c-RARP group. In addition, the RS-RARP group had a shorter console time (mean difference = −16.28, 95% CI: −27.04 to −5.53, P = 0.003) and a lower incidence of hernia (OR = 0.35, 95% CI: 0.19–0.67, P = 0.001). However, there were no significant differences in estimated blood loss, pelvic lymph node dissection rate, postoperative complications, 1-year-biochemical recurrence rate, and postoperative sexual function.Conclusions: Compared with c-RARP, RS-RARP showed better recovery of continence, shorter console time, and lower incidence of hernia. Although there was no significant difference in overall PSM, we suggest that the surgeon should be more careful if the lesion is in the anterior prostate.
Objective To develop and externally validate a novel nomogram in biopsy‐naïve patients with prostate‐specific antigen (PSA) <10 ng/ml and PI‐RADS v2.1 = 3 lesions. Methods We retrospectively collected 307 men that underwent initial biopsy from October 2015 to January 2022 in Cohort 1 (The First Affiliated Hospital of Soochow University). External cohort (Cohort 2, Kunshan Hospital) included 109 men that met our criteria from July 2016 to June 2021. By Slicer‐3D Software, the volume of all lesions was divided into two subgroups (PI‐RADS v2.1 = 3a and 3b). Logistic regression analysis was performed to screen for variables and construct nomogram by analyzing clinical data from Cohort 1. Receiver operating characteristics curve analysis, calibration plot and decision curve analysis (DCA) were plotted to validate the nomogram in external cohort. Results A total of 70 (22.8%) patients was diagnosed with prostate cancer in Institution 1. Among them, 34 (11.1%) had clinically significant prostate cancer (csPCa). Age, prostate‐specific antigen density, digital rectal examination, PI‐RADS v2.1 = 3 subgroups (3a and 3b) and apparent diffusion coefficient (ADC, <750 mm 2 /s) were predictive factors for prostate cancer (PCa) and csPCa. High area under the curve of the nomogram was found in Cohort 1 and Cohort 2 for PCa (0.857 vs. 0.850) and for csPCa (0.896 vs. 0.893). Calibration curves showed excellent agreement between the predicted probability and actual risk for the models in internal and external validation. The DCA demonstrated net benefit of our nomogram. Conclusion Until now, this is the first nomogram that predicts PCa and csPCa in biopsy‐naïve patients with PSA <10 ng/ml and PI‐RADS v2.1 = 3 lesions. Furthermore, PI‐RADS v2.1 = 3 subgroups were considered to be an independent risk factor in our model. Our nomogram may assist urologists in biopsy decision making for these so‐called “double gray zone” patients.
Objective. In this study, we used the TCGA database and ICGC database to establish a prognostic model of iron death associated with renal cell carcinoma, which can provide predictive value for the identification of iron death-related genes and clinical treatment of renal clear cell carcinoma. Methods. The gene expression profiles and clinical data of renal clear cell carcinoma and normal tissues were obtained in the TCGA database and ICGC database, and the differential genes related to iron death were screened out. The differential genes were screened out by single and multifactor Cox risk regression model. R software, “edge” package (version 4.0), was used to identify the DELs of 551 transcriptional gene samples and 522 clinical samples. The risk prediction model with genes was established to analyze the correlation between the genes in the established model and clinical characteristics, Through the final screening of iron death related genes, it can be used to predict the prognosis of renal clear cell carcinoma and provide advice for clinical targeted therapy. Results. Seven iron death differential genes (CLS2, FANCD2, PHKG2, ACSL3, ATP5MC3, CISD1, PEBP1) associated with renal clear cell carcinoma were finally screened and were refer to previous relevant studies. These genes are closely related to iron death and have great value for the prognosis of renal clear cell carcinoma. Conclusion. Seven iron death genes can accurately predict the survival of patients with renal clear cell carcinoma.
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