Increased Plasma Ignition Distance Practice may Prevent the Obturator Reflex Occurrences and Compare of its Effectiveness Versus Obturator Block: A Prospective, Randomized, Controlled Study.

OBJECTIVE: To compare the ability of the obturator nerve block (ONB) and increased plasma ignition distance practice (IPDP) techniques to inhibit obturator nerve reflex (ONR) occurring with bipolar transurethral resection of the bladder. METHODS: Sixty patients who had a tumor placed at the lateral sidewall or had a tumor in another part of the bladder along with the lateral wall were randomly enrolled. Cystoscopic and ultrasonographic examinations and a computerized tomography scanning of the urinary bladder were used to determine the ONB side. Group 1 consisted of patients who had the ONB procedure. Group 2 consisted of patients who had IPIDP. The severity of the ONR was classified as severe, mild, and very mild. The study's primary endpoint was ONR occurrences and successful completion of the surgery. The secondary endpoints were bleeding and bladder perforation. RESULTS: There was a significant difference in the occurrence of ONR between the two groups (P = 0.0011). However, there was no significant difference between the two groups in the ability to resect the tumor and complete the surgery (P = .764). There was no correlation between the ONR and the tumor size (P = 0.478). CONCLUSION: Our study concluded that both ONB and IPIDP have comparable results, especially in resecting tumors and completing the operation. IPIDP has some advantages over ONB, such as shorter operative time, lower total costs, and less trained personnel requirements.

Bladder Urothelial Carcinoma: Machine Learning-based Computed Tomography Radiomics for Prediction of Histological Variant.

RATIONALE AND OBJECTIVES: Histological variant (HV) of bladder urothelial carcinoma (UC) is a significant factor for therapy management. We aim to assess the predictive performance of machine learning (ML)-based Computed Tomography radiomics of UC for HV. MATERIALS AND METHODS: Volume of interest of 37 bladder UC tumors, of which 21 were pure and 16 were HV, were manually segmented. The extracted first- and second-order texture features (n = 117) using 3-D Slicer radiomics were compared to the radical cystectomy histopathological results. ML algorithms were performed to determine the significant models using Python 2.3, Pycaret library. The sample size was increased to 74 by synthetic data generation, and three outliers from the training set were removed (training dataset; n = 52, test dataset; n = 19). The predictive performances of 15 ML algorithms were compared. Then, the best two models were evaluated on the test set and ensembled by Voting Classifier. RESULTS: The ML algorithms demonstrated area under curve (AUC) and accuracy ranging 0.79-0.97 and 50%-90%, respectively on the train set. The best models were Gradient Boosting Classifier (AUC: 0.95, accuracy: 90%) and CatBoost Classifier (AUC: 0.97, accuracy: 85%). On the test set; the Voting Classifier of these two models demonstrated AUC, accuracy, recall, precision, and F1 scores as follows; 0.93, 79%, 86%, 67%, and 75%, respectively. CONCLUSION: ML-based Computed Tomography radiomics of UC can predict HV, a prognostic factor that is indeterminable by qualitative radiological evaluation and can be missed in the preoperative histopathological specimens.