A randomized clinical trial evaluating the short-term results of ureteral stent encrustation in urolithiasis patients undergoing ureteroscopy: micro-computed tomography evaluation

Kidney, ureter, and urinary bladder segmentation based on non-contrast enhanced computed tomography images using modified U-Net

This study was performed to segment the urinary system as the basis for diagnosing urinary system diseases on non-contrast computed tomography (CT). This study was conducted with images obtained between January 2016 and December 2020. During the study period, non-contrast abdominopelvic CT scans of patients and diagnosed and treated with urinary stones at the emergency departments of two institutions were collected. Region of interest extraction was first performed, and urinary system segmentation was performed using a modified U-Net. Thereafter, fivefold cross-validation was performed to evaluate the robustness of the model performance. In fivefold cross-validation results of the segmentation of the urinary system, the average dice coefficient was 0.8673, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9651, 0.7172, and 0.9196, respectively. In the test dataset, the average dice coefficient of best performing model in fivefold cross validation for whole urinary system was 0.8623, and the dice coefficients for each class (kidney, ureter, and urinary bladder) were 0.9613, 0.7225, and 0.9032, respectively. The segmentation of the urinary system using the modified U-Net proposed in this study could be the basis for the detection of kidney, ureter, and urinary bladder lesions, such as stones and tumours, through machine learning.

Establishment of an artificial urine model in vitro and rat or pig model in vivo to evaluate urinary crystal adherence

The current study aimed to establish an experimental model in vitro and in vivo of urinary crystal deposition on the surface of ureteral stents, to evaluate the ability to prevent crystal adhesion. Non-treated ureteral stents were placed in artificial urine under various conditions in vitro. In vivo, ethylene glycol and hydroxyproline were administered orally to rats and pigs, and urinary crystals and urinary Ca were investigated by Inductively Coupled Plasma-Optical Emission Spectrometer. in vitro, during the 3- and 4-week immersion periods, more crystals adhered to the ureteral stent in artificial urine model 1 than the other artificial urine models (p < 0.01). Comparing the presence or absence of urea in the composition of the artificial urine, the artificial urine without urea showed less variability in pH change and more crystal adhesion (p < 0.05). Starting the experiment at pH 6.3 resulted in the highest amount of crystal adhesion to the ureteral stent (p < 0.05). In vivo, urinary crystals and urinary Ca increased in rat and pig experimental models. This experimental model in vitro and in vivo can be used to evaluate the ability to prevent crystal adhesion and deposition in the development of new ureteral stents to reduce ureteral stent-related side effects in patients.

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.

An in vitro bladder model with physiological dynamics: Vesicoureteral reflux alters stent encrustation pattern

In vitro models are indispensable to study the physio-mechanical characteristics of the urinary tract and to evaluate ureteral stent performances. Yet previous models mimicking the urinary bladder have been limited to static or complicated systems. In this study, we designed a simple in vitro bladder model to simulate the dynamics of filling and voiding. The physio-mechanical condition of the model was verified using a pressure-flow test with different bladder outlet obstruction levels, and a reflux test was performed to qualitatively demonstrate the stent associated vesicoureteral reflux (VUR). Finally, the setup was applied with and without the bladder model to perform encrustation tests with artificial urine on commercially available double-J stents, and the volumes of luminal encrustations were quantified using micro-Computed Tomography and image segmentation. Our results suggest that, VUR is an important factor contributing to the dynamics in the upper urinary tract with indwelling stents, especially in patients with higher bladder outlet obstruction levels. The influence of VUR should be properly addressed in future in vitro studies and clinical analyses.

Advances in ureteral stent technology

PURPOSE OF REVIEW

Ureteral stents are an invaluable tool for urologists but suffer from several drawbacks, including: limited durability due to stent encrustation, significant morbidity from bothersome urinary symptoms, pain and infection, risk of the forgotten stent and costly removal. This review highlights key advances in ureteral stent technology that seek to address these issues.

RECENT FINDINGS

Over the past 2 years clinical trials have brought ureteral stent technology aimed to reduce stent-associated morbidity closer to clinical application. Stent material and changes to stent design show promise in improving patient tolerability without increasing complications. Low-tech innovations such as magnetic and catheter snare removal may quickly reduce costs. Radical advances such as biodegradable stent materials remain in preclinical models but show promise for eliminating the need for stent removal.

SUMMARY

The ideal ureteral stent does not currently exist in clinical practice. This review highlights key studies that have advanced ureteral stent technology in the past 2 years.

Silicone vs. Polyurethane Stent: The Final Countdown

Ureteric stents are conventionally used in daily urological practice. There is ongoing debate on the superiority of different stent materials, particularly in terms of patient tolerance. We conducted a literature review to compare silicone stents and stents made of other materials from a patient tolerability perspective. We conclude that silicone stents are better tolerated but further research is required.

Imaging and Chemical Analysis of External and Internal Ureteral Stent Encrustation

Introduction

Ureteral stents are effective in alleviating flow disruptions in the urinary tract, whether due to ureteral stones, strictures or extrinsic ureteral obstruction. However, significant stent encrustation on the external and/or internal stent lumen walls can occur, which may interfere with stent functioning and/or removal. Currently, there is only limited, generally qualitative, information on the distribution, mineral structure, and chemical content of these deposits, particularly in terms of stent lumen encrustation.

Objective

To quantify, in an initial investigation, external and internal encrustation in representative, intact ureteral stents. The study investigates possible correlations between patterns of external and internal encrustation, determines mineral structure and chemical composition, and examines the potential for stent lumen obstruction even in the absence of external stent wall encrustation.

Study Design

High-resolution, laboratory micro-computed tomography (micro-CT) was used to non-destructively image external and internal stent encrustation in four representative stents. X-ray diffractometry (XRD) and scanning electron microscopy–energy dispersive x-ray spectroscopy (SEM-EDS) enabled parallel analysis of mineral structure and chemical content of samples collected from external and internal encrusted material along the distal, proximal and mid-ureteral stent regions.

Results

Extensive stent lumen encrustation can occur within any region of a stent, with only incidental or minor external encrustation, along the entire length of the stent. External and internal encrusted materials in a given stent are generally similar, consisting of a combination of amorphous (mostly organic) and crystalline mineral deposits.

Conclusion

Micro-CT demonstrates that significant stent lumen encrustation can occur, which can lead to partial or full stent lumen occlusion, even when the exterior stent wall is essentially free of encrusted material.