Outcomes Associated with the Endourological Management of Stent Encrustation: Findings from a Literature Review on Behalf of the EAU YAU Urolithiasis Group

Massella, Virginia; Juliebø‐Jones, Patrick; Pietropaolo, Amelia; Beisland, Christian; Somani, Bhaskar

doi:10.1007/s11934-023-01144-x

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Taguchi also commented on the limitations of patient’s habitus not being uniform [ 14 ]. Some patients had preoperative stenting which was mentioned to have possibly influenced the complication rate reported [ 26 , 27 ]. Due to their age and possibly some related dementia, anaesthesia related complications such as delirium could also be underreported.…”

Section: Discussionmentioning

confidence: 99%

Is ureteroscopy and active stone treatment safe and effective in octogenarians? A review of current literature

Bhatnagar,

Nedbal,

Somani

2024

Arab Journal of Urology

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Background With the aging of our patient population, and the increasing incidence of kidney stone disease in the elderly, active stone treatment is becoming more common. In this review of current literature, we aim to assess safety and efficacy of ureteroscopy (URS) as primary treatment for urolithiasis in the octogenarians. Materials and methods A scoping review of literature according to the PRISMA guidelines was performed, using the relevant search terms. Original articles were screened and included. A narrative review of the studies is provided, with emphasis on outcomes of URS in the elderly. Results 10 studies were included in the analysis. URS performed in the elderly population showed a good safety and efficacy, with stone-free rates (SFR) comparable to the general population. URS specific complication rates seems to be comparable to the other age groups, with postoperative events mostly related to anaesthesia and pre-existing medical conditions. The overall complication rate was still low, with a slightly prolonged hospital stay. Predictors for SFR were age, severe comorbidities and stone burden. Conclusion URS for stone treatment in the elderly population is safe and effective, with comparable surgical outcomes to that of the general population. As comorbidities play an important role in the fitness for surgery and overall survival, risks and benefit of active stone treatment should be carefully balanced in this group.

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Section: Discussionmentioning

confidence: 99%

Is ureteroscopy and active stone treatment safe and effective in octogenarians? A review of current literature

Bhatnagar,

Nedbal,

Somani

2024

Arab Journal of Urology

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“…Endourological management of ureteral stent encrustation remains technically and strategically challenging ( 28 ). Multimodal surgery is generally required.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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ObstructiveTo develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics.MethodsA 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.Results1,409 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.ConclusionMachine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.

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A method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis

Hu,

Yan,

Liu

et al. 2024

Front. Physiol.

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ObjectiveTo develop and validate a method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis.MethodAll 222 cases of ureteral stent data were obtained from the Fifth Affiliated Hospital of Sun Yat-sen University. Firstly, a neural network was used to detect the region of the ureteral stent, and the results of the coarse detection were completed and connected domain filtered based on the continuity of the ureteral stent in 3D space to obtain a 3D segmentation result. Secondly, the segmentation results were analyzed and detected based on the 3D morphology, and the centerline was obtained through thinning the 3D image, fitting and deriving the ureteral stent, and obtaining radial sections. Finally, the abnormal areas of the radial section were detected through polar coordinate transformation to detect the encrustation area of the ureteral stent.ResultsFor the detection of ureteral stent encrustations in the ureter, the algorithm’s confusion matrix achieved an accuracy of 79.6% in the validation of residual stones/ureteral stent encrustations at 186 locations. Ultimately, the algorithm was validated in 222 cases, achieving a ureteral stent segmentation accuracy of 94.4% and a positive and negative judgment accuracy of 87.3%. The average detection time per case was 12 s.ConclusionThe proposed medical CT image ureteral stent wall stone detection method based on Mask-RCNN and 3D morphological analysis can effectively assist clinical doctors in diagnosing ureteral stent encrustations.

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Outcomes Associated with the Endourological Management of Stent Encrustation: Findings from a Literature Review on Behalf of the EAU YAU Urolithiasis Group

Cited by 3 publications

References 35 publications

Is ureteroscopy and active stone treatment safe and effective in octogenarians? A review of current literature

Is ureteroscopy and active stone treatment safe and effective in octogenarians? A review of current literature

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

A method for detecting ureteral stent encrustations in medical CT images based on Mask-RCNN and 3D morphological analysis

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