ArticleFrontiers in medicine2023
Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Nephrostomy-Assisted Multimodal Removal of an Embedded Ureteral Metallic Stent: A Case Report.Cureus · 2025Article
- Toward Supportive Decision-Making for Ureteral Stent Removal: Development of a Morphology-Based X-Ray Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Development and validation of a radiomics-based nomogram for predicting pathological grade of upper urinary tract urothelial carcinoma.BMC cancer · 2024Article
- A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.Tomography (Ann Arbor, Mich.) · 2024Article
- A computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.Cancer medicine · 2024Article
- A method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis.Frontiers in physiology · 2024Article
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Authors and funding
10 authors.
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Abstract
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 ( 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.
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