Could radiological parameters help to predict the failure of ureteral access sheath placement.

To identify the radiological parameters which may help to predict the success of ureteral access sheath (UAS) placement during retrograde intrarenal surgery (RIRS).The study included 49 patients in whom failure ureteral access sheath placement in RIRS and 49 control group patients who were successfully placement between January 2023 and December 2023. The age, gender, body mass index (BMI), non-contrast computed tomography (NCCT), and kidney ureter bladder (KUB) radiographs were compared between the two groups. Measurements of the anteroposterior (ap) diameter of the pelvic inlet, anteroposterior diameter of the pelvic outlet, interspinous distance diameter were taken from non-contrast computed tomography (NCCT), while pelvic anteroposterior diameter and pelvic lateral diameter were measured from kidney ureter bladder (KUB) radiography. There were no significant differences between the groups in age, gender, body mass index, ap pelvic inlet diameter, ap pelvic outlet, and interspinous distance diameter. However, a statistically significant difference was found between the pelvic ap diameter and pelvic lateral diameter values measured on the KUB radiography. The values for pelvic ap diameter and pelvic lateral diameter measured in the KUB radiographs can be used to predict the likelihood of UAS passage during RIRC procedures. However, further studies with larger patient groups are needed to establish a cut-off value.

Association between chinese visceral adiposity index and risk of kidney stones in a health screening population: an ultrasonography based cross-sectional study.

BACKGROUND: Obesity is an important risk factor for kidney stones(KS). Chinese Visceral Adiposity Index (CVAI), as a specific indicator for visceral obesity in the Chinese population, can more accurately assess the visceral fat content in Chinese individuals compared to Visceral Adiposity Index (VAI). However, the association between CVAI and risk for KS has not been studied. METHODS: A total of 97,645 participants from a health screening cohort underwent ultrasound examinations for the diagnosis of kidney stones, along with measurements of their CVAI. Logistic regressions were utilized to determine the relationship between different quartiles of CVAI and the incidence of kidney stones. Simultaneously, subgroup analysis and the computation of dose-response curves were employed to pinpoint susceptible populations. RESULTS: Among the participants, 2,888 individuals (3.0%) were diagnosed with kidney stones. The mean CVAI values ± standard deviation for the four groups were: Q1 (18.42 ± 19.64), Q2 (65.24 ± 10.39), Q3 (98.20 ± 9.11), and Q4 (140.40 ± 21.73). In the fully adjusted multivariable model, CVAI was positively correlated with urolithiasis (OR = 1.001; 95% CI = 1.000, 1.002). Compared with the first quartile of CVAI, the population in the fourth quartile of CVAI had a higher prevalence of kidney stones (OR = 1.231; 95% CI = 1.066, 1.415). Through subgroup analysis, a positive correlation between CVAI and the risk of kidney stones was found in non-smokers (OR = 1.001, 95%CI:1.000, 1.002), non-drinkers (OR = 1.001, 95%CI:1.000, 1.002), non-hypertensive subgroups (OR = 1.003, 95%CI:1.002, 1.003), and non-diabetes subgroups (OR = 1.001, 95%CI:1.000, 1.002). CONCLUSION: The findings suggest that CVAI could be a reliable and effective biomarker for assessing the potential risk of kidney stone prevalence, with significant implications for the primary prevention of kidney stones and public health.

CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were randomly allocated to training and validation sets at a 7:3 ratio. Radiomic features were extracted using 3Dslicer software. The least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features from the 107 extracted features, and logistic regression, decision tree, AdaBoost, and support vector machine (SVM) models were subsequently used to construct radiomic feature prediction models. Among these, the logistic regression algorithm demonstrated the best predictive performance and stability. The area under the curve (AUC) of the logistic regression model based on radiomic features was 0.858 in the training cohort and 0.806 in the validation cohort. Furthermore, univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for kidney stones, which were gender and body mass index (BMI). Combining these independent risk factors improved the predictive performance of the model, with AUC values of 0.860 in the training cohort and 0.814 in the validation cohort. Clinical decision curve analysis (DCA) indicated that the radiomic model provided clinical benefit when the probability ranged from 0.2 to 1.0. The radiomic model has a good ability to screen high-risk patients with kidney stones, facilitating early intervention in kidney stone cases and improving patient prognosis.

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis.

In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

Dynamic renal scans as a modality for follow-up of flexible ureteroscopy.

OBJECTIVE: To determine whether dynamic renal scans - DTPA or MAG3 - routinely performed after flexible ureteroscopies (f-URS) could detect the development of an obstruction and thus promote prompt early intervention for kidney preservation. PATIENTS AND METHODS: In this retrospective study, with all the data recorded prospectively between April 2010 and October 2023, 250 renal units in 242 patients with upper urinary tract stones (UUTS) who underwent ureterorenoscopy by one surgeon in the same medical center were evaluated. Stone-free rate (SFR) was defined as no residual fragments at all using an intraoperative "triple test". The following characteristics were examined: gender, BMI, age, Hounsfield unit, stone diameter, laterality, renal/ureteral stones, stone-free rate, and auxiliary procedures per renal unit. The Clavien-Dindo classification was used to report complications. Renal units with residual stones were scheduled for a 2nd f-URS. Post- flexible ureteroscopy ureteral obstruction and renal function were detected using renal scan DTPA or MAG-3. The primary outcome was renal/ ureteral obstruction. RESULTS: The mean patient age was 53 years. The mean stone size was 12.3 mm. Stones in renal pelvis, upper, middle and lower calyces were treated in 9.2% (23), 27.6% (69), and 30.8% (77) of cases, respectively; 44% (110) ureteral stones were also treated. The single- and second-session SFRs were 94.8% and 99.7%, respectively. A third auxiliary procedure was needed in one renal unit (0.4%). The mean number of procedures per renal unit was 1.06 (264/250). Ureteral double-J stents were inserted in 53.6% (134) of the cases. In 37 (14.8%) cases, a stent was placed before surgery. Post-operative complications were minor, with readmission and pain control needed in only two patients (0.8%). No avulsion or perforation of the ureters was observed. In six patients with t1/2 between 10-20 minutes, a second renal scan revealed spontaneous improvement and no obstruction in five patients. One patient with large stones and a history of prior ureteroscopy developed a ureteral stricture (0.4%) and needed treatment with laser endoureterotomy. CONCLUSIONS: Post-flexible ureteroscopy obstruction due to ureteral stricture is very rare. A routine renal scan post-operatively may be used in potentially high-risk patients.

A machine learning approach using stone volume to predict stone-free status at ureteroscopy.

INTRODUCTION: To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS). MATERIAL AND METHODS: Retrospective analysis of patients undergoing URS for kidney stone disease at our institution from 2012 to 2021. SF status was defined as stone fragments < 2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments > 2 mm at XR KUB or US KUB at 3 months follow up. We specifically included all non-SF patients to optimise our algorithm for identifying instances with residual stone burden. SF patients were also randomly sampled over the same time period to ensure a more balanced dataset for ML prediction. Stone volumes were measured using preprocedural CT and combined with 19 other clinical and radiological factors. A bagged trees machine learning model with cross-validation was used for this analysis. RESULTS: 330 patients were included (SF: n = 276, not SF: n = 54, mean age 59.5 ± 16.1 years). A fivefold cross validated RUSboosted trees model has an accuracy of 74.5% and AUC of 0.82. The model sensitivity and specificity were 75% and 72.2% respectively. Variable importance analysis identified total stone volume (17.7% of total importance), operation time (14.3%), age (12.9%) and stone composition (10.9%) as important factors in predicting non-SF patients. Single and cumulative stone size which are commonly used in current practice to guide management, only represented 9.4% and 4.7% of total importance, respectively. CONCLUSION: Machine learning can be used to predict patients that will be SF at the time of URS. Total stone volume appears to be more important than stone size in predicting SF status. Our findings could be used to optimise patient counselling and highlight an increasing role of stone volume to guide endourological practice and future guidelines.

The safety and efficacy of Sotn ureteroscopy for renal and upper ureteral calculi: a prospective multicenter randomized controlled trial.

BACKGROUND: Sotn ureteroscopy is a new lithotripsy procedure developed on the basis of ureteroscopy and includes a rigid ureteral access sheath, standard mirror, lithotripsy mirror, and Sotn perfusion aspirator. Thus, we performed a prospective multicenter randomized controlled trial comparing the safety and efficacy of Sotn ureteroscopy in the treatment of renal and upper ureteral calculi. METHODS: In this study, 224 patients with renal and upper ureteral calculi were randomly divided equally into study and control groups from March 2018 to March 2022. All the patients were approved by the hospital ethics committee (proof number: ZF-2018-164-01 and ZF-2018-165-01) of the Second Affiliate Hospital of Guangzhou University of Chinese Medicine in China. The primary outcome was stone-free rate (SFR) assessed by computed tomography on the 1st day and month after treatment and operation duration. The secondary outcome was postoperative complication rate. RESULTS: In total, for upper ureteral calculi, the SFR of 1 day after operation of the Sotn ureteroscopy group was significantly higher than the rigid ureteroscopy group (83.6% vs. 60%, P=0.006). Moreover, operative time (33.7±1.80 vs. 52.9±2.73 min, P<0.005) of the Sotn ureteroscopy group was significantly lower than the rigid ureteroscopy group. Additionally, the SFR of 1 day after operation and operative time for the study group (Sotn ureteroscopy combined with flexible ureteroscopy) and the control group (flexible ureteroscopy alone) were 63.2% and 36.8% (P=0.005), 65.6±4.06 and 80.3±4.91 (P=0.023), respectively. However, there were no significant differences in the SFR of 1 month after operation, success rate of ureteral access sheath placement, and postoperative complications between the two groups (P>0.05). In subgroups with stone diameters ≥1.5 cm and stone CT values ≥1000 Hounsfield units, Sotn ureteroscopy showed more advantages in terms of the SFR of 1 day after operation. Importantly, complications such as ureteral injury, sepsis, fever, and severe hematuria were not statistically different between the two groups (P>0.05). CONCLUSIONS: For renal and upper ureteral calculi, Sotn ureteroscopy has the advantage of a higher SFR of 1 day after the operation and a shorter operative time, suggesting that the Sotn ureteroscopy may have further potential applications in clinics.

The mean Hounsfield unit range acquired from different slices produces superior predictive accuracy for pyonephrosis in obstructive uropathy.

PURPOSE: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. MATERIALS AND METHODS: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (ΔuHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. RESULTS: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 mm² vs. 877.23 mm², p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher ΔuHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The ΔuHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). CONCLUSIONS: Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the ΔuHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

Ultrasound accuracy in evaluating renal calculi in Maysan province.

Renal calculi are a common clinical presentation. While ultrasound (US) is a widely used imaging modality for kidney stone diagnosis due to its accessibility and lower cost, its accuracy compared to computerized tomography (CT), the gold standard, remains understudied. This cross-sectional study evaluated the diagnostic accuracy of ultrasound for detecting and characterizing kidney stones compared to computed tomography (CT). Fifty-six patients with suspected kidney stones based on flank pain underwent abdominal ultrasound to assess stone presence, size, location, and the severity of any hydronephrosis (kidney swelling). These findings were then confirmed with a subsequent non-contrast CT scan. There was a fair agreement between US and CT (Kappa = 0.368) for detecting the stone location. The US could not detect 7 (12.5%) stones, being less sensitive in the middle and upper calyx compared to CT. There was a fair agreement between the US and CT (Kappa = 0.394) for detecting the severity of hydronephrosis. The US was less sensitive to moderate and severe hydronephrosis compared to CT. The abdominal ultrasound demonstrated excellent reliability for stone size measurement (intraclass correlation = 0.924), with CT measurements only slightly larger on average (mean difference 0.9 mm). Although abdominal ultrasound provides reliable stone size assessment, its capacity to accurately localize stones and assess hydronephrosis severity is limited.

Ultrasound-guided renal puncture followed by endoscopically guided tract dilatation vs standard fluoroscopy-guided percutaneous nephrolithotomy for non-opaque renal stones; a randomized clinical trial.

This study was designed to evaluate the non-inferiority of ultrasound puncture followed by endoscopically guided tract dilatation compared to the standard fluoroscopy-guided PCNL. Forty patients with non-opaque kidney stones eligible for PCNL were randomly divided into two groups. The standard fluoroscopy-guided PCNL using the Amplatz dilator was performed in the XRAY group. In the SONO group, the Kidney was punctured under an ultrasound guide followed by tract dilatation using a combination of the Amplatz dilator based on the tract length and an endoscopically guided tract dilatation using a bi-prong forceps in cases of short-advancement. The primary outcome was successful access. In 90% of cases in the XRAY and 95% in the SONO group access dilatation process was performed uneventfully at the first attempt (p = 0.5). In 45% of cases in the SONO group, bi-prong forceps were used as salvage for short-advancement. In one case in the X-ray group over-advancement occurred. One month after surgery, the stone-free rate on the CT-scan was 75% for the X-ray group and 85% for the SONO group (p = 0.4). There were no significant differences in operation time, hospitalization duration, transfusion, or complication rates between the two groups. We conclude that ultrasound-guided renal puncture, followed by endoscopically guided tract dilatation can achieve a high success rate similar to X-ray-guided PCNL while avoiding the harmful effects of radiation exposure and the risk of over-advancement.

Intraoperative computed tomography for detection of residual stones in endourology procedures: systematic review and meta-analysis.

BACKGROUND: Success rates in endourological procedures, notably percutaneous nephrolithotomy (PCNL) and ureteroscopy (URS), have demonstrated suboptimal outcomes, leading to more reinterventions and radiation exposure. Recently, the use of intraoperative computed tomography (ICT) scans has been hypothesized as a promising solution for improving outcomes in endourology procedures. With this considered, we conducted a comprehensive systematic review and meta-analysis encompassing all available studies that evaluate the impact of the use of intraoperative CT scans on surgical outcomes compared to conventional fluoroscopic-guided procedures. METHODS: This systematic review was conducted in accordance with PRISMA guidelines. Multiple databases were systematically searched up to December of 2023. This study aimed to directly compare the use of an ICT scan with the standard non-ICT-guided procedure. The primary endpoint of interest was success rate, and the secondary endpoints were complications and reintervention rates, while radiation exposure was also evaluated. Data extraction and quality assessment were performed following Cochrane recommendations. Data was presented as an Odds ratio with 95%CI across trials and a random-effects model was selected for pooling of data. RESULTS: A comprehensive search yielded 533 studies, resulting in the selection of 3 cohorts including 327 patients (103 ICT vs 224 in non-ICT). Primary outcome was significantly higher in the experimental group versus the control group (84.5% vs 41.4% respectively, 307 patients; 95% CI [3.61, 12.72]; p<0.00001; I2=0). Reintervention rates also decreased from 32.6% in the control to 12.6% in the ICT group (OR 0.34; 95%CI [0.12,0.94]; p =0.04; I2= 48%), whereas complication rates did not exhibit significant differences. Radiation exposure was also significantly reduced in two of the included studies. CONCLUSION: This meta-analysis highlights a favorable outcome with intraoperative CT scan use in PCNL procedures, showing a considerable increase in SFR when compared to standard fluoroscopy and nephroscopy. Despite limited studies, our synthesis underscores the potential of ICT scans to significantly reduce residual stones and their consequences for endourology patients, as reinterventions and follow-up ionizing radiation studies.

Ureteral wall thickness as a predictor for non-invasive treatment success for steinstrasse. Can we save time?

PURPOSE: We aimed to define factors affecting the non-invasive overall treatment success (medical expulsive therapy (MET) ± shock wave lithotripsy (SWL)) for uncomplicated ureteral steinstrasse (SS) clearance. METHODS: We retrospectively evaluated consecutive patients who underwent SWL for renal stones between 2017 and 2021. Patients with uncomplicated SS were included. All patient's demographic and radiological data, e.g., age, gender, pre-SWL stenting, SS site, type, leading stone size in widest diameter (< 10 mm and ≥ 10 mm), ureteral wall thickness (UWT) in mm against the leading stone were collected. If SS was diagnosed, medical treatment was given for 4 weeks. In case of MET failure, either SWL for the leading stones + MET or direct URS was done. Non-invasive treatment success (SFR) was considered if complete clearance of SS occurred with no complications or the need for invasive intervention. RESULTS: A total of 145 patients were included with mean age of 45.9 ± 12.4 years. SFR in case of MET only occurred in 27.9%. Complications happened in 26 patients (17.9%). Non-invasive treatment SFR was achieved in 78 patients (53.8%) totally where SS type I, leading stone size ≤ 10 mm type and decreased UWT around the leading stone increased treatment success. CONCLUSION: Ureteral wall thickness is an important factor predicting SS management success. Besides the decreased UWT, non-invasive management should be offered for type I SS with leading stone ≤ 10 mm.

Evaluation of Technologist-Controlled Factors Used During CT Kidney Stone Examinations.

PURPOSE: To identify technologist-controlled factors to decrease dose and improve image quality and evaluate their use during computed tomography (CT) kidney stone examinations. METHODS: Online scholarly databases were searched to acquire peer-reviewed, published articles involving methods of optimizing radiation dose during CT. These articles were reviewed, and the technologist-controlled factors identified were protocol selection, patient centering in the bore, and scan length. The author retrospectively reviewed CT kidney stone examinations performed at a free-standing emergency department to evaluate the use of these factors. RESULTS: Technologists consistently chose the correct scan protocol. Reviewed literature was used to determine the acceptable variance for positioning at isocenter and overscanning beyond anatomical landmarks. All patient positioning was off-center in the vertical direction, and in 3 of those examinations, patient positioning was off-center more than the 3 cm threshold. Horizontal off-center positioning was less frequent. All examinations had some amount of overscan, with 73.1% of patients being overscanned more than the determined threshold of 10% of total scan length. DISCUSSION: Accurate labeling of protocols at the console assist technologists in choosing protocols correctly. Technologists were inconsistent with patient centering and scan range. The amount of which images were off-center was consistent with previous research studies, while the amount of overscan was less than that found in previous studies. CONCLUSION: Technologists have an important role in optimizing patient radiation dose. Education and quality assurance could help technologists gain awareness of these factors and use them effectively.

Advancements in Uric Acid Stone Detection: Integrating Deep Learning with CT Imaging and Clinical Assessments in the Upper Urinary Tract.

INTRODUCTION: Among upper urinary tract stones, a significant proportion comprises uric acid stones. The aim of this study was to use machine learning techniques to analyze CT scans and blood and urine test data, with the aim of establishing multiple predictive models that can accurately identify uric acid stones. METHODS: We divided 276 patients with upper urinary tract stones into two groups: 48 with uric acid stones and 228 with other types, identified using Fourier-transform infrared spectroscopy. To distinguish the stone types, we created three types of deep learning models and extensively compared their classification performance. RESULTS: Among the three major types of models, considering accuracy, sensitivity, and recall, CLNC-LR, IMG-support vector machine (SVM), and FUS-SVM perform the best. The accuracy and F1 score for the three models were as follows: CLNC-LR (82.14%, 0.7813), IMG-SVM (89.29%, 0.89), and FUS-SVM (29.29%, 0.8818). The area under the curves for classes CLNC-LR, IMG-SVM, and FUS-SVM were 0.97, 0.96, and 0.99, respectively. CONCLUSION: This study shows the feasibility of utilizing deep learning to assess whether urinary tract stones are uric acid stones through CT scans, blood, and urine tests. It can serve as a supplementary tool for traditional stone composition analysis, offering decision support for urologists and enhancing the effectiveness of diagnosis and treatment.

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence.

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.