Can Double J stent encrustation be predicted by risk analysis and nomogram?: A retrospective case-control study.

To explore the risk factors and develop a nomogram to predict Double J stent encrustation incidence. The general demographic characteristics and underlying risk factors of 248 patients with upper urinary tract calculus who underwent endoscopic lithotripsy and Double J stenting at the Fifth Affiliated Hospital of Sun Yat-Sen University between January 1st, 2018 and January 1st, 2023 were retrospectively analyzed. Among them,173 patients were randomly selected to form the development cohort. A multivariate logistic regression model was employed to identify the independent risk factors associated with Double J stent encrustation, and a nomogram was developed for predicting its occurrence. Additionally, 75 patients were randomly selected to form the validation cohort to validate the nomogram. Multivariate logistic regression analysis revealed that several factors were significantly associated with Double J stent encrustation: indwelling time (odds ratio [OR]1.051; 95% confidence interval [CI] 1.030-1.073, P < .001), urine PH (OR 2.198; 95% CI 1.061-4.539, P = .033), fasting blood glucose (OR 1.590; 95% CI 1.300-1.943, P < .001), and total cholesterol (OR 2.676; 95% CI 1.551-4618, P < .001).Based on these findings, A nomogram was developed to predict the occurrence of Double J stent encrustation. The nomogram demonstrated good performance with an area under the curve of 0.870 and 0.862 in the development and validation cohorts, respectively. Furthermore, the calibration curve indicated a well-fitted model. We constructed and validated an accessible nomogram to assist urologists in evaluating the risk factors associated with Double J stent encrustation and predicting its likelihood.

Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study.

Obstructive: To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics. Methods: A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models. Results: 1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models. Conclusion: Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.