Single-port robot-assisted perineal radical prostatectomy with the da Vinci XI system: initial experience and learning curve using the cumulative sum method.

BACKGROUND: To evaluate the early functional and oncological outcomes of single-port robot-assisted perineal radical prostatectomy (sp-pRARP) using the da Vinci XI system and analyze its learning curve using the cumulative sum (CUSUM) method. METHODS: The clinical data of 50 patients who underwent sp-pRARP for localized prostate cancer between May 2020 and May 2022 in our center by a single surgeon were analyzed retrospectively. Demographic information, preoperative and postoperative variables, complications, early functional and oncological outcomes of patients were recorded. The CUSUM method was used to illustrate the learning curve based on operation time. RESULTS: All surgeries were completed without conversion. The median (interquartile range, IQR) operation time was 205.0 (82.5) min, whereas the median (IQR) docking time was 30.0 (15.0) min and the console time was 120.0 (80.5) min. The median (IQR) estimated blood loss (EBL) was 50.0 (137.5) mL. Positive surgical margins were detected in five patients (10.0%). The continence rate was 40.9%, 63.6%, 88.4%, and 97.7% at the 1, 3, 6, and 12 months after surgery. According to the CUSUM plot, the inflection points of the learning curve were 20 cases, splitting the case series into "early phase" and "late phase." In "late phase" cases, there was less time spent on each step of the operation and less EBL. CONCLUSIONS: Sp-pRARP using the da Vinci XI system was verified to be a feasible and reliable surgical approach. According to the CUSUM plot, 20 cases was considered the turning point for surgeons to master the novel technique.

Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network.

BACKGROUND AND OBJECTIVE: Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technology, magnetic resonance images (MRI) have been used in the diagnosis and treatment of prostate cancer because of its clarity and non-invasiveness. Prostate MRI segmentation and diagnosis experience problems such as low tissue boundary contrast. The traditional segmentation method of manually drawing the contour boundary of the tissue cannot meet the clinical real-time requirements. How to quickly and accurately segment the prostate tumor has become an important research topic. METHODS: This paper proposes a prostate tumor diagnosis based on the deep learning network PSP-Net+VGG16. The deep convolutional neural network segmentation method based on the PSP-Net constructs a atrous convolution residual structure model extraction network. First, the three-dimensional prostate MRI is converted to two-dimensional image slices, and then the slice input of the two-dimensional image is trained based on the PSP-Net neural network; and the VGG16 network is used to analyze the region of interest and classify prostate cancer and normal prostate. RESULTS: According to the experimental results, the segmentation method based on the deep learning network PSP-Net is used to identify the data set samples. The segmentation accuracy is close to the Dice similarity coefficient and Hausdorff distance, and even exceeds the traditional prostate image segmentation method. The Dice index reached 91.3%, and the technique is superior in speed of processing. The predicted tumor markers are very close to the actual markers manually by clinicians; the classification accuracy and recognition rates of prostate MRI based on VGG16 are as high as 87.95% and 87.33%, and the accuracy rate and recall rate of the network model are relatively balanced. The area under curve index is also higher than other models, with good generalization ability. CONCLUSION: Experiments show that prostate cancer diagnosis based on the deep learning network PSP-Net+VGG16 is superior in accuracy and processing time compared to other algorithms, and can be well applied to clinical prostate tumor diagnosis.

Image Enhancement Model Based on Deep Learning Applied to the Ureteroscopic Diagnosis of Ureteral Stones during Pregnancy.

OBJECTIVE: To explore the image enhancement model based on deep learning on the effect of ureteroscopy with double J tube placement and drainage on ureteral stones during pregnancy. We compare the clinical effect of ureteroscopy with double J tube placement on pregnancy complicated with ureteral stones and use medical imaging to diagnose the patient's condition and design a treatment plan. METHODS: The image enhancement model is constructed using deep learning and implemented for quality improvement in terms of image clarity. In the way, the relationship of the media transmittance and the image with blurring artifacts was established, and the model can estimate the ureteral stone predicted map of each region. Firstly, we proposed the evolution-based detail enhancement method. Then, the feature extraction network is used to capture blurring artifact-related features. Finally, the regression subnetwork is used to predict the media transmittance in the local area. Eighty pregnant patients with ureteral calculi treated in our hospital were selected as the research object and were divided into a test group and a control group according to the random number table method, 40 cases in each group. The test group underwent ureteroscopy double J tube placement, and the control group underwent ureteroscopy lithotripsy. Combined with the ultrasound scan results of the patients before and after the operation, the operation time, time to get out of bed, and hospitalization time of the two groups of patients were compared. The operation success rate and the incidence of complications within 1 month after surgery were counted in the two groups of patients. RESULTS: We are able to improve the quality of the images prior to medical diagnosis. The total effective rate of the observation group was 100.0%, which is higher than that of the control group (90.0%). The difference between the two groups was statistically significant (P < 0.05). The adverse reaction rate in the observation group was 5.0%, which was lower than 17.5% in the control group. The difference between the two groups was statistically significant (P < 0.05). The comparison results are then prepared. CONCLUSIONS: The image enhancement model based on deep learning is able to improve medical diagnosis which can assist radiologists to better locate the ureteral stones. Based on our method, double J tube placement under ureteroscopy has a significant effect on the treatment of ureteral stones during pregnancy, and it has good safety and is worthy of widespread application.

Association between N-Acetyltransferase 2 Polymorphism and Bladder Cancer Risk: a Meta-Analysis in a Single Ethnic Group.

BACKGROUND: Many studies have evaluated the correlation between N-acetyltransferase 2 (NAT2) slow acetylation genotype and bladder cancer risk. However, the results are inconsistent and remain to be confirmed in each ethnic group. To assess the effects of NAT2 acetylation status on the risk of bladder cancer in the Chinese population, a meta-analysis was performed. METHODS: Studies were identified using PubMed and Chinese databases through February 2016. The associations were assessed with pooled odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: This meta-analysis included 10 studies with 896 bladder cancer cases and 1188 controls. In the overall analysis, NAT2 slow acetylation phenotype was significantly associated with an increased risk of bladder cancer in the Chinese population (OR = 1.68, 95% CI = 1.11 - 2.53). In the subgroup analyses by geographic areas and sources of controls, significant risk was found in Mainland China (OR = 1.83, 95% CI = 1.04 - 3.20) and hospitalbased studies (OR = 1.74, 95% CI = 1.27 - 2.38), but not in Taiwan China. CONCLUSIONS: This meta-analysis suggested that the NAT2 slow acetylation genotype is associated with an increased bladder cancer risk in Chinese individuals.

Comparison of intravesical bacillus Calmette-Guerin and mitomycin C administration for non-muscle invasive bladder cancer: A meta-analysis and systematic review.

The aim of the present meta-analysis was to compare the benefits of Bacillus Calmetter-Guerin (BCG) and mitomycin C in the treatment of patients with superficial bladder cancer. The present meta-analysis analyzed the benefits of BCG and mitomycin C in the treatment of patients with superficial bladder cancer by comparing progression-free survival (PFS) rates in patients treated with either of the drugs following transurethral resection. The Medline, Cochrane and EMBASE databases were searched between January 1966 and August 31, 2014 for studies that investigated the efficacy of the intravesical instillation of chemotherapy in patients with non-muscle invasive bladder cancer who had been treated with transurethral resection. Search terms included: 'Urinary bladder neoplasms', 'superficial bladder cancer' and 'non-muscle invasive bladder cancer'; 'bacillus Calmette-Guerin' or 'BCG'; 'mitomycin C'; and 'intravesical administration'. Sensitivity and data quality analyses were performed. A total of 6 randomized controlled studies were included with 1,289 patients. Complete 5-year PFS data for patients who received intravesical resection and were treated with mitomycin C or BCG was provided for 3 of the 6 studies, which were therefore included in the meta-analysis. The overall analysis revealed a significant benefit of BCG compared with mitomycin C in terms of 5-year PFS rate (odds ratio, 0.53; 95% confidence interval, 0.38-0.75; P<0.001), indicating that BCG was superior to mitomycin C therapy in patients with non-muscle invasive bladder cancer following transurethral resection.