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 ( = 189), internal validation cohort ( = 81) and external validation cohort ( = 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).