Diagnostic value of urine cyclic RNA-0071196 for bladder urothelial carcinoma.

OBJECTIVE: To investigate the diagnostic value of urine cyclic RNA-0071196 (circRNA-0071196) in the patients with bladder urothelial carcinoma (BUC). METHOD: The expression of circRNA-0071196 was detected in the urine samples using qRT-PCR from 40 BUC patients and 30 non-UBC patients at our department from December 2018 to September 2021. The expression difference of circRNA-0071196 was compared between the two groups, and the relationship between the expression of circRNA-0071196 in the urine of UBC patients and the clinical pathological characteristics was analyzed. RESULTS: (1) The expression of circRNA-0071196 in the urine of BUC group was significantly higher than that in the non-BUC group (P < 0.05). (2) The expression of circRNA-0071196 in the urine of BUC group was not related to age, sex, or lymph node metastasis (P > 0.05). (3) The expression of circRNA-0071196 in the urine of BUC group was related to tumor T stage, tumor grade and muscle invasion. (4) The urine circRNA-0071196 expression effectively distinguished BUC patients from non-BUC patients. CONCLUSION: The elevated expression of urine circRNA-0071196 in BUC patients indicates that circRNA-0071196 has promising potential as a non-invasive urinary biomarker for detecting BUC.

A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo.

Preoperative diagnosis of urinary infection stones is difficult, and accurate detection of stone composition can only be performed ex vivo. To provide guidance for better perioperative management and postoperative prevention of infection stones, we developed a machine learning model for preoperative identification of infection stones in vivo. The clinical data of patients with urolithiasis who underwent surgery in our hospital from January 2011 to December 2015 and January 2017 to December 2021 were retrospectively analyzed. A total of 2565 patients were included in the study, and 1168 eligible patients with urinary calculi were randomly divided into training set (70%) and test set (30%). Five machine learning algorithms (Support Vector Machine (SVM), Multilayer Perceptron (MLP), Decision Tree (DT), Random Forest Classifier (RFC), and Adaptive Boost (AdaBoost)) and 14 preoperative variables were used to construct the prediction model. The performance measure was the area under the receiver operating characteristic curve (AUC) of the validation set. The importance of 14 features in each prediction model for predicting infection stones was analyzed. A total of 89 patients (5.34%) with infection stones were included in the validation set. All the five prediction models showed strong discrimination in the validation set (AUC: 0.689-0.772). AdaBoost model was selected as the final model (AUC: 0.772(95% confidence interval, 0.657-0.887); Sensitivity: 0.522; Specificity: 0.902), UC positivity, and urine pH value were two important predictors of infection stones. We developed a predictive model through machine learning that can quickly identify infection stones in vivo with good predictive performance. It can be used for risk assessment and decision support of infection stones, optimize the disease management of urinary calculi and improve the prognosis of patients.