Objective:To establish the prediction model of postoperative Gleason score (GS) risk of prostatic cancer (PCa), and to compare the diagnostic efficacy of the model and each independent risk factor for PCa medium-high risk group.Methods:The clinical data of 362 patients who accepted transrectal prostate biopsy in the Run Run Shaw Hospital Affiliated to Zhejiang University School of Medicine from January 2018 to December 2019 were analyzed retrospectively, and a total of 343 patients with prostate cancer who met the enrollment criteria were selected. According to the GS grading system, these patients were divided into low risk group, moderate risk group and high risk group. At first, the single factor analysis and Spearman rank correlation were used to find out the effective indicators with good correlation with GS risk. Then, multiple linear regression equation was applied for multi-factor analysis to obtain the independent risk factors and the prediction model for predicting GS risk, and then the ROC curve was used to compare the diagnostic efficacy of each independent risk factor and prediction model for PCa medium-high risk group.Results:In the single factor analysis, the differences of all indicators in GS risk were statistically significant (all P<0.05). In the correlation analysis with GS risk, except for the indicators of prostate volume (all P>0.05), the other indexes had linear correlations with the different risks of GS (all P<0.05). Among them, the total prostate specific antigen and two-dimensional ultrasound (2D-US) score showed moderate positive correlations( rs=0.402, 0.579, all P<0.001), contrast enhanced ultrasound (CEUS) score showed a high positive correlation ( rs=0.709, P<0.001), and the rest indexes showed low positive correlations. Multiple linear regression was used to obtain two independent risk factors of 2D-US score ( X1) and CEUS score ( X2) for the prediction of GS risk, then, a prediction model was established: Y=0.863+ 0.066 X1+ 0.27 X2, the corresponding linear coefficient differences were statistically significant(all P<0.05). By the ROC analysis, the areas under the curves of 2D-US score, CEUS score and the prediction model were 0.838, 0.906 and 0.907, respectively. Conclusions:2D-US score and CEUS score are independent risk factors for predicting postoperative GS risk, and the diagnostic efficacy of the prediction model is higher than those of the 2D-US score and CEUS score for the medium-high risk group.

Objective To explore the learning curve of robotic-assisted kidney transplantation (RAKT). Methods The clinical data of 96 consecutive RAKT patients performed by the same surgical team were retrospectively analyzed. The arterial anastomosis time, venous anastomosis time, ureteral anastomosis time, hospital stay, and blood loss were selected as evaluation indicators. The learning curve of RAKT was analyzed using the cumulative sum (CUSUM), and the curve was divided into the learning improvement stage and the proficient mastery stage according to the learning curve. The learning curve was verified by comparing the general data and surgical data of patients in different learning stages, and the clinical efficacy of each stage was analyzed. Results The optimal fitting equation of the learning curve reached its peak at the 33rd case, which was the minimum number of surgeries required to master RAKT. There was no statistically significant difference in age, gender, dialysis type, previous abdominal surgery history, number of donor renal arteries, and preoperative serum creatinine between the learning improvement group and the proficient mastery group (all P>0.05). Compared with the learning improvement stage, the body mass index (BMI) was higher, and the number of right donor kidney was increased compared to the left donor kidney in the proficient mastery stage (both P<0.05). There were no significant differences in arterial anastomosis time, ureteral anastomosis time, postoperative serum creatinine, and complications between the two groups (all P>0.05). The iliac vessel dissection time, warm ischemia time, venous anastomosis time, blood loss, and hospital stay in the proficient mastery stage were superior to those in the learning improvement stage, with statistically significant differences (all P<0.05). Conclusions RAKT requires at least 33 cases to cross the learning curve. There is no difference in complications and recovery of transplant renal function between the learning improvement stage and the proficient mastery stage.