Variation Coefficient of Stone Density: A Novel Predictor of the Outcome of Extracorporeal Shockwave Lithotripsy

CT-based radiomics for predicting success of shock wave lithotripsy in ureteral stones larger than 1 cm

PURPOSE

This study aims to investigate the predictive value of CT-based radiomics in determining the success of extracorporeal shock wave lithotripsy (SWL) treatment for ureteral stones larger than 10mm in adult patients.

MATERIALS AND METHODS

A total of 301 eligible patients (165/136 successful/unsuccessful) who underwent SWL were retrospectively evaluated and divided into a training cohort (n = 241) and a test cohort (n = 60) following an 8:2 ratio. Univariate analysis was performed to assess clinical characteristics for constructing a nomogram. Radiomics and conventional radiological characteristics of stones were evaluated. Following feature selection, radiomics and radiological models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), K nearest neighbor (KNN), and XGBoost. The models' performance was compared using metrics such as the area under the receiver operating characteristic curve (AUC), precision, recall, accuracy, and F1 score. Finally, a nomogram was created incorporating the best image model signature and clinical predictors.

RESULTS

The SVM-based radiomics model showed superior predictive performance in both training and test cohorts (AUC: 0.956, 0.891, respectively). The nomogram, which combined SVM-based radiomics signature with proximal ureter diameter (PUD), demonstrated further improved predictive performance in the test cohort (AUC: 0.891 vs. 0.939, P = 0.166).

CONCLUSIONS

Integration of CT-derived radiomics and PUD showed excellent ability to predict SWL treatment success in patients with ureteral stones larger than 10mm, providing a promising approach for clinical decision-making.

Variation coefficient of stone density and renal cortical thickness: the parameters evaluating non-contrast computed tomography imaging for predict extracorporeal shock wave lithotripsy success

The stone density (SD) is not the same in all parts of the stone due to the heterogeneous nature of the stone and the shock wave (SW) passes through tissues of many different densities until it reaches the stone. These factors affect the success of Extracorporeal Shock Wave Lithotripsy (ESWL). We aimed to evaluate the effect of the Variation Coefficient of Stone Density (VCSD) and Renal Cortical Tickness (RCT) on the success of ESWL. Between 2020 and 2023, 510 patients who underwent ESWL were divided into 2 groups treatment success (n:304) and treatment failure (n:206). Non-Contrast Computed Tomography (NCCT) imaging values of hydronephrosis degree of the kidney, stone location, stone volume (SV), stone-skin distance (SSD), SD, Standard deviation of Stone Density (SDSD), VCSD, RCT, Soft-Tissue Thickness (STT), Muscle Thickness (MT) were analyzed. VCSD value was obtained by dividing SDSD by SD. Along the SW, tissues were divided into three components: kidney (renal cortex), muscle and other soft tissues. RCT, MT and SSD were measured at three different angles (0°, 45°, and 90°) and these 3 lengths were averaged. In univariate analysis, Body Mass Index (BMI), SV, SD, VCSD, SSD, RCT and STT were demonstrated to affect ESWL success. In multivariate analysis, low BMI, SV, SD, RCT and large VCSD were significant independent predictors of ESWL success. Among these parameters, VCSD had the highest prediction accuracy, followed by SD, SV, RCT and BMI, respectively. This study demonstrated that VCSD value and RCT are predictive parameters in determining the treatment of patients with urinary calculi and selecting suitable ESWL candidates.

AI prediction of extracorporeal shock wave lithotripsy outcomes for ureteral stones by machine learning-based analysis with a variety of stone and patient characteristics

We propose an artificial intelligence prediction method for extracorporeal shock wave lithotripsy treatment outcomes by analysis of a wide variety of variables. We retrospectively reviewed the records of 171 patients from between January 2009 and November 2019 that underwent shock wave lithotripsy at Wakayama Medical University, Japan, for ureteral stones shown on preoperative non-contrast computed tomography. This prediction method consisted of stone area extraction, stone analyzing factor extraction from non-contrast computed tomography images, and shock wave lithotripsy treatment result prediction by a non-linear support vector machine for analysis of 15 input and automatic measurement factors. Input factors included patient age, skin-to-stone distance, and maximum ureteral wall thickness, and the automatic measurement factors included 11 non-contrast computed tomography image texture factors in the stone area and stone volume. Permutation feature importance was also applied to the artificial intelligence prediction results to analyze the importance of each factor relating to estimate decision grounds. The prediction performance was evaluated by five-fold cross-validation, it obtained 0.742 of the mean area under the receiver operating characteristic curve. The proposed method is shown by these results to have robust data diversity and effective clinical application. As a result of permutation feature importance, some factors that showed high p-values in the significant difference tests were thought to have a high contribution to the proposed prediction method. Future issues include validation using a larger volume of high-resolution clinical non-contrast computed tomography image data and the application of deep learning.

A Prospective Study on the Impact of Clinical Factors and Adjusted Triple D System for Success Rate of ESWL

Objective: Our study aimed to evaluate the success rate of ESWL and identify relevant treatment-specific factors affecting treatment outcomes, as well as to assess the accuracy of the updated Triple D scoring system and compare it with older systems. Material and Methods: A prospective study of 71 patients who received ESWL treatment for renal stones that were 5-15 mm in size was completed. The patient having no residual stones or residual stones lesser than 4 mm after ESWL was identified as a treatment success. Univariate and multivariate logistic regression and ROC curves were used to identify important factors for treatment outcomes. Results: Successful treatment was achieved for 66.2% of patients. The stone volume (SV), mean stone density (MD), and delivered power to the stone volume unit ratio (SMLI/SV) were defined as the most critical factors influencing ESWL success. An updated Triple D score system with a, SMLI/SV ratio could be an alternative to older systems and reach an even higher accuracy. A limitation of this study is the limited sample size due to the COVID-19 pandemic. Conclusions: Our results show that the three factors that most influence the success of ESWL are the stone size, mean stone density, and SMLI/SV ratio. Based on this, we present a simple updated triple D score system to predict ESWL success, which could be implemented in future clinical practice.

Pure uric acid stone prediction model using the variant coefficient of stone density measured by thresholding 3D segmentation-based methods: A multicenter study

Urinary stones are common urological diseases with increasing prevalence and incidence worldwide. Among the various types of stones, uric acid stones can be dissolved by oral chemolysis without any surgical procedure. Therefore, our study demonstrates that variant coefficient of stone density measured by thresholding a three-dimensional segmentation-based method from noncontrast computed tomography images can be used to identify pure uric acid stones from non-pure uric acid stones. This study provides a preoperative pure uric acid stone prediction model that could reduce invasive procedural treatments. The pure uric acid stone prediction model may offer optimized clinical decision-making for patients with urinary stones.

BACKGROUND AND OBJECTIVES

While most urinary stones are managed with interventional therapy, uric acid (UA) stones can be dissolved by oral chemolysis without invasive procedures. This study aimed to develop and validate a pure UA (pUA) stone prediction model using a variant coefficient of stone density (VCSD) measured by thresholding a three-dimensional (3D) segmentation-based method.

METHODS

Patients with urolithiasis treated at Keimyung University Dongsan Hospital between January 2017 and December 2020 were divided into training and internal validation sets, and patients from Kyungpook National University Hospital between January 2017 and December 2018 were used as an external validation set. Each stone was segmented by a thresholding 3D segmentation-based method using an attenuation threshold of 130 Hounsfield units. VCSD was calculated as the stone heterogeneity index divided by the mean stone density.

RESULTS

A total of 1175 urinary stone cases in 1023 patients were enrolled in this study. Of these, 224 (19.1%) were pUA stone cases. Among the potential predictors, thresholding 3D segmentation-based VCSD, age, sex, radio-opacity, hypertension, diabetes, and urine pH were identified as independent pUA stone predictors, and VCSD was the most powerful indicator. The pUA stone prediction model showed good discrimination, yielding area under the receiver operating characteristic curve of 0.960 (95% confidence interval (CI): 0.940-0.979, P < 0.001), 0.931 (95% CI: 0.875-0.987, P < 0.001), and 0.938 (95% CI: 0.912-0.965, P < 0.001) in the training, internal validation, and external validation sets, respectively.

CONCLUSIONS

VCSD measured using 3D segmentation was a decisive independent predictive factor for pUA stones. Furthermore, the established prediction model with VCSD can serve as a noninvasive preoperative tool to identify pUA stones.

Development and validation of a predictive model for stone-free failure after extracorporeal shockwave lithotripsy in patients with ureteral stone in a large prospective cohort

PURPOSE

To develop and validate a nomogram for predicting stone-free failure after shock wave lithotripsy (SWL) guided by ultrasound in patients with ureteral stones.

METHODS

The development cohort consisted of 1698 patients who underwent SWL guided by ultrasound at our center from June 2020 through August 2021. Multivariate unconditional logistic regression analysis was used for building a predictive nomogram with regression coefficients. An independent validation cohort consisted of 712 consecutive patients from September 2020 through April 2021. The performance of the predictive model was assessed in regard to discrimination, calibration, and clinical usefulness.

RESULTS

Predictors of stone-free failure included distal stone location (odds ratio = 1.540, P < 0.001), larger stone size (odds ratio = 1.722, P < 0.001), higher stone density (odds ratio = 1.722, P < 0.001), larger skin to stone distance (SSD) (odds ratio = 1.058, P < 0.001), and higher grade of hydronephrosis (odds ratio = 1.755, P = 0.010). For the validation cohort, the model showed good discrimination with an area under the receiver operating characteristic curve of 0.925 (95% confidence interval, 0.898, 0.953) and good calibration (unreliability test, P = 0.412). Decision curve analysis demonstrated that the model was also clinically useful.

CONCLUSIONS

This study demonstrated that stone location, stone size, stone density, SSD, and hydronephrosis grade were significant predictors of stone-free failure after SWL guided by ultrasound in patients with ureteral stones. This may guide clinical practice.

A New Parameter for Calcium Oxalate Stones: Impact of Linear Calculus Density on Non-Contrast Computed Tomography

Background and Objectives: Non-contrast computed tomography (NCCT) is widely used to evaluate urolithiasis. The NCCT attenuation, measured in Hounsfield units (HU), has been evaluated to predict stone characteristics. We propose a novel parameter, linear calculus density (LCD), and analyze variables from NCCT imaging to predict calcium oxalate (CaOx) stones, which are common and challenging to fragment. Materials and Methods: We retrospectively reviewed the medical records of patients with urolithiasis between 2014 and 2017. Among those, 790 patients were included. Based on the NCCT pre-treatment, the maximal stone length (MSL), mean stone density (MSD), and stone heterogeneity index (SHI) were obtained. In addition, the variation coefficient of stone density (VCSD = SHI/MSD × 100) and linear calculus density (LCD = VCSD/MSL) were calculated. In accordance with the stone analysis, the patients were divided into two groups (CaOx and non-CaOx groups). The logistic regression model and receiver operating characteristic (ROC) curve were used for predictive modeling. Results: In the CaOx group, the SHI, VCSD, and LCD were more significant than in the non-CaOx group (all p < 0.001). SHI (OR 1.002, 95% CI 1.001-1.004, p < 0.001), VCSD (OR 1.028, 95% CI 1.016-1.041, p < 0.001), and LCD (OR 1.352, 95% CI 1.270-1.444, p < 0.001) were significant independent factors for CaOx stones in the logistic regression models. The areas under the ROC curve for predicting CaOx stones were 0.586 for SHI, 0.66 for VCSD, and 0.739 for LCD, with a cut-point of 2.25. Conclusions: LCD can be a useful new parameter to provide additional information to help discriminate CaOx stones before treatment.

The impact of Hounsfield unit-related variables on percutaneous nephrolithotomy outcomes

We aimed to identify the association between Hounsfield Unit(HU)-related variables and percutaneous nephrolithotomy (PCNL) outcomes. We enrolled patients with single renal stones (1-3 cm) who underwent single-tract PCNL between January 2014 and October 2019. Demographics and stone characteristics were retrospectively reviewed. Preoperative computerized tomography (CT) and follow-up CT within at least 3 months after PCNL were included in this analysis. Stone-free status was defined as residual stone measuring ≤ 2 mm within 3 months postoperatively. HU and cross-sectional area (CSA) were measured using the free-draw technique. We analyzed HU-related variables using logistic regression model for outcomes. Altogether, 188 out of 683 patients met the inclusion criteria. The stone-free rate (SFR) was 79.2%. There were no significant differences in age, sex, BMI, ASA class, laterality, pre-op shockwave lithotripsy, stone size, stone burden, skin-to-stone distance, and HU between the stone-free and remnant groups. CSA and HU/CSA in the stone-free and remnant groups were 94.5 ± 46.1 and 128.3 ± 98.5 (p = 0.043) and 10.1 ± 5.6 and 7.3 ± 3.4 (p = 0.001), respectively. Multivariate logistic regression analysis revealed that pelvis, ureteropelvic junction stones, and HU/CSA were independent predictors of SFR. HU did not affect PCNL outcomes. We believe that HU/CSA could be used for determining stone treatment plans and predicting outcomes.

Extremely slow, half-number shockwave lithotripsy for ureteral stones

To elucidate and compare the efficacy and safety of the reduced (30 shocks/min with 1200 shocks/session) and standard protocols (60 shocks/min with 2400 shocks/session) of extracorporeal shockwave lithotripsy (SWL) for ureteral stones treatment. This study was a retrospective review of 2410 SWL procedures with reduced or standard protocols (groups R and S) in 1106 patients for ureteral stones between March 2014 and March 2021. The primary outcome was treatment success, defined as the absence of residual fragments on ultrasonography and plain radiography within 30 and 90 days. A multivariate logistic regression and propensity score matching analysis evaluated the association between the reduced protocol and treatment success. This study included 311 and 544 patients in the reduced (R) and standard (S) protocol groups, respectively. The patient's characteristics were comparable, excluding the lithotripter machine. No significant difference was observed between groups R and S in treatment success rates within 30 (63.3 vs. 65.8%, p = 0.50) and 90 days (88.7 vs. 91.5%, p = 0.18). The multivariate analysis indicated no significant association between reduced protocol and treatment success within 30 and 90 days (p = 0.44 and p = 0.68, respectively). Propensity score matching showed no significant difference in the treatment success rates within 30 and 90 days. The Extremely slow, half number protocol outcomes are comparable to those of the standard protocol for treating ureteral stones.

Success of extracorporeal shock wave lithotripsy based on CT texture analysis

OBJECTIVE

The aims of the study were to evaluate whether computerised tomography texture analysis (CTTA) based on non-contrast computed tomography (NCCT) has predictive value for the success of extracorporeal-shockwave lithotripsy (ESWL) in upper urinary tract stones (UUTS).

METHODS

This study included 156 of 356 patients undergoing ESWL for UUTS sized 0.5-2 cm from 2015 to 2019. Patients with congenital kidney anomalies, radiolucent stones, multiple stones, treated for upper urinary tract stones previously and lower pole stones were excluded from study. The number of ESWL sessions of the patients was as follows: 78 (50%) patients had 1 session, 30 (19.2%) patients had 2 sessions and 48 (30.8%) patients had >2 sessions. First- and second-order CTTA properties of patients' UUTS were evaluated using texture analysis software (LIFEx Software). Other clinical features such as Hounsfield Unit (HU), initial stone size, body-mass index (BMI) and skin to stone distance (SSD) was recorded. The patients were divided into two groups according to ESWL success. Cases with residual stones larger than 4 mm were considered failed cases.

RESULTS

BMI, the standard deviation of HU, SSD, skewness, kurtosis, entropy and all second-order statistics were found to be statistically different between the two groups except for correlation (P < .05). Multivariate analysis showed longer SSD and four new parameters of CTTA (kurtosis, entropy, dissimilarity and energy by the distribution of pixel grey levels in the UUTS) to be significant predictors for unsuccessful ESWL outcomes. SSD and second-order CTTA properties (dissimilarity and energy) had an area under ROC curve of 0.802, 0.850 and 0.824 at a 95% confidence interval. ESWL success rate in all patients was 76.9%.

CONCLUSION

CTTA can help select patients who will undergo ESWL for upper urinary tract stones. Thus, we can reduce treatment costs and ESWL complications by preventing unnecessary ESWL applications.

Variables measured on three-dimensional computed tomography are preferred for predicting the outcomes of shock wave lithotripsy

PURPOSE

Shock wave lithotripsy (SWL) is used to treat upper urinary tract stones. Recently, some volume analyzers have enabled preoperative assessment using three-dimensional computed tomography (3D-CT). We evaluated the efficacy of 3D-CT variables for predicting the outcomes of SWL.

METHODS

The study population included 193 patients who underwent SWL between November 2014 and August 2020. In addition to conventional two-dimensional computed tomography (2D-CT) assessments, 3D-CT assessments of targeted stones were retrospectively performed, and stone size and stone density (SD) were measured. The successful and unsuccessful treatment groups were compared and risk factors for an unsuccessful first SWL session were investigated. The predictive accuracy of variables measured on 3D-CT was evaluated by receiver operating characteristic curves and multivariate analyses.

RESULTS

The success rate of the first SWL session was 73.1%. Stone volume, mean SD and highest SD on 3D-CT were significantly higher in the unsuccessful group than in the successful group. Stone volume showed a higher area under the curve (AUC) than the estimated volumetric stone burden and stone diameter, which were measured on 2D-CT (0.729, 0.683, and 0.672, respectively). The AUCs of the mean SD and highest SD on 3D-CT were higher than those on 2D-CT (0.699, 0.680, 0.617, and 0.627, respectively). Multivariate analyses identified stone volume (≥ 0.29 ml), mean SD on 3D-CT (≥ 421 HU), and absence of hydronephrosis as independent predictive factors for unsuccessful SWL.

CONCLUSION

3D-CT variables were promising predictors of the outcomes of SWL. Preoperative 3D-CT assessment is helpful for selecting favorable patients for SWL.

Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning

Background

Though shock wave lithotripsy (SWL) has developed to be one of the most common treatment approaches for nephrolithiasis in recent decades, its treatment planning is often a trial-and-error process based on physicians’ subjective judgement. Physicians’ inexperience with this modality can lead to low-quality treatment and unnecessary risks to patients.

Objective

To improve the quality and consistency of shock wave lithotripsy treatment, we aimed to develop a deep learning model for generating the next treatment step by previous steps and preoperative patient characteristics and to produce personalized SWL treatment plans in a step-by-step protocol based on the deep learning model.

Methods

We developed a deep learning model to generate the optimal power level, shock rate, and number of shocks in the next step, given previous treatment steps encoded by long short-term memory neural networks and preoperative patient characteristics. We constructed a next-step data set (N=8583) from top practices of renal SWL treatments recorded in the International Stone Registry. Then, we trained the deep learning model and baseline models (linear regression, logistic regression, random forest, and support vector machine) with 90% of the samples and validated them with the remaining samples.

Results

The deep learning models for generating the next treatment steps outperformed the baseline models (accuracy = 98.8%, F1 = 98.0% for power levels; accuracy = 98.1%, F1 = 96.0% for shock rates; root mean squared error = 207, mean absolute error = 121 for numbers of shocks). The hypothesis testing showed no significant difference between steps generated by our model and the top practices (P=.480 for power levels; P=.782 for shock rates; P=.727 for numbers of shocks).

Conclusions

The high performance of our deep learning approach shows its treatment planning capability on par with top physicians. To the best of our knowledge, our framework is the first effort to implement automated planning of SWL treatment via deep learning. It is a promising technique in assisting treatment planning and physician training at low cost.

Atlas of scoring systems, grading tools and nomograms in Endourology: A comprehensive overview from The TOWER Endourological Society research group

INTRODUCTION

With an increase in the prevalence of kidney stone disease (KSD), there has been a universal drive to develop reliable and user-friendly tools such as grading systems and predictive nomograms. An atlas of scoring systems, grading tools and nomograms in Endourology is provided in this paper.

METHODS

A comprehensive search of world literature was performed to identify nomograms, grading systems and classification tools in endourology related to KSD. Each of these were reviewed by the authors and have been evaluated in a narrative format with details on those which are externally validated and their respective citation count on google scholar.

RESULTS

A total of 54 endourological tools have been described in our atlas of endourological scoring systems, grading tools and nomograms. Of the tools, 23 (43%) are published in the last 3 years showing an increasing interest in this area. This includes 5 for percutaneous nephrolithotomy (PCNL), 6 for flexible ureteroscopy (fURS), 3 for semi-rigid URS (sURS), 9 for shockwave lithotripsy (SWL), 2 for stent encrustations, 3 for intra-operative appearance at the time of URS and 3 to classify intra-operative ureteric injury. There were 3 tools for renal colic assessment, one each for prediction of future stone event, stone classification and stone impaction and 2 for need of emergency intervention in ureteric stone. While 2 tools are related to stone recurrence, 6 are related to post-procedural complications. There are now 2 tools for simulation in endourology and 5 for patient reported outcome measures (PROMS).

CONCLUSIONS

A number of reliable and established tools exist currently in endourology. Each of these offers their own respective advantages and disadvantages. While nomograms and scoring systems can help in the decision making, these must be tailored to individual patients based on their specific clinical scenarios, expectations and informed consent.

Outcome groups and a practical tool to predict success of shock wave lithotripsy in daily clinical routine

PURPOSE

To improve outcome prediction of extracorporeal shock wave lithotripsy (SWL) by development of a model based on easily available clinical and radiographical predictors and suitable for daily clinical use.

MATERIALS AND METHODS

We evaluated predictive factors for SWL success in 517 consecutive patients suffering from urinary calculi who underwent SWL between 2010 and 2018. Analyses included descriptive statistics, receiver operating characteristic statistics and logistic regression. Predictive value was improved by combining parameters using model selection and recursive partitioning.

RESULTS

Of the 517 patients, 310 (60.0%) had a successful SWL. Best individual predictor of SWL success was mean attenuation (MAV), with an area under the curve (AUC) of 0.668, and an optimal cutpoint (OC) of 987.5 HU. The best multivariable model, including MAV, stone size, skin to stone distance (SSD), presence of an indwelling stent, and four interaction effects, yielded an AUC of 0.736. Recursive partitioning would categorize patients into three outcome groups with high (76.9%), intermediate (41%) and low (10%) success probability. High probability of SWL success (76.9%) was found for patients with a stone with MAV ≤ 987 HU or with MAV > 987 HU but stone size ≤ 11 mm and SSD (45°) ≤ 88 mm.

CONCLUSION

A model based on four established predictors, and provided as an Excel^®-Tool, can clearly improve prediction of SWL success. In addition, patients can be classified into three defined outcome groups based on simple cutpoint combinations. Both tools improve informed decision-making in daily clinical practice and might reduce failure rates.

Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success?

OBJECTIVE

To explore whether multiple 3D computed tomography texture analysis (3D-CTTA) parameters can predict the therapeutic effects of holmium: YAG laser lithotripsy (LL) on ureteral calculi.

METHODS

The files from 94 patients (102 stones) with proximal ureteral calculi treated only by LL at a single institution were retrospectively retrieved from January 2016 to March 2019. According to intra-operative observations and postoperative reexamination, samples were divided into a completely crushed and a non-crushed group. Preoperative non-contrast-enhanced computed tomography (NCCT) images obtained by multiple CT scanners were imported to MaZda software for 3D texture analysis (TA). The CT-derived value of each target stone was measured, and 15 TA parameters were extracted by delineating volumes of interest (VOIs). Receiver operating characteristic (ROC) curves were drawn to determine the optimal critical value of each parameter based on the Youden index, and univariable and multivariable logistic regression analyses determined the significant factors for LL success.

RESULTS

In univariable analysis, significant differences (p < 0.05) were observed among 7 parameters. In multivariable analysis, Perc.01 3D > 2062 (p = 0.03) and Z-fraction of image in runs (Z-Fraction) > 0.45570 (p = 0.009) were significant independent predictors, with odds ratios (ORs) of 24.204 and 60.329, respectively. In subgroup analysis based on the cutoff value of the CT-derived value (HU = 960), Perc.01 3D (OR = 44.154, 95% CI (2.379, 819.618), p = 0.011) and Z-Fraction (OR = 14.519, 95% CI (2.088, 100.953), p = 0.007) remained statistically significant.

CONCLUSIONS

The combination of 3D-CTTA parameters and the CT-derived value can be used as a quantitative reference to predict whether a target stone could be completely crushed by LL.

KEY POINTS

• Computed tomography texture analysis (CTTA) may be helpful in selecting suitable laser lithotripsy (LL) patients. • 3D-CTTA better predicts stone fragility than commonly used methods (such as the CT-derived value). • The combination of CTTA and the CT-derived value can be used as a preoperative quantitative reference.

The role of extracorporeal shock wave lithotripsy in the future of stone management

PURPOSE OF REVIEW

The most relevant recent findings on the use of extracorporeal shock wave lithotripsy (ESWL) in adult population to provide an insight of its role in the current and future of stone treatment. Comparing ESWL with other modalities is not in the scope of this review.

RECENT FINDINGS

We conducted a PubMed/Embase search and reviewed recent publications that include relevant information on the development of ESWL. Low-rate shock waves improve stone breakage, ramping energy modalities improve stone fragmentation and have lower incidence of hematoma and kidney injury. Transgluteal approach is suggested to improve stone-free rates for distal ureteral stones in a single session. Proper coupling is the most important technical aspect of the treatment and coupling improvement can be achieved by optical monitorization. Triple D score is a promising tool in proper patient selection, but external validation is needed. Predictive information arising from computed tomography scans has been refined by the variant coefficient of stone density and 3D texture analysis that might improve outcomes in the future.

SUMMARY

Recent evidence suggests that modifying techniques and protocols, and better patient selection are the current trends for improving ESWL outcomes. EWSL will keep its role as the single noninvasive treatment in stone management with room for outcome improvement in the future.

Traditional Chinese medicine on treating ureteral calculi: A systematic review and meta-analysis protocol

BACKGROUND

Ureteral calculi generally refer to the temporary obstruction of the human body after the ureteral stenosis. When the ureteral stones are not discharged in time, they can grow in the original site, causing the patient to have corresponding clinical manifestations, such as: renal colic, hematuria, etc, when severe, can cause renal obstruction and hydronephrosis, seriously endangering the patient's health. Ureteral calculi usually occur in young and middle-aged people. The peak age of the disease is between 20 and 50 years old. It also occurs in the young and middle-aged labor force. The men incidence rate is 2 to 3 times that of women. Ureteral calculi is one of the current refractory diseases, and the effect after treatment with integrated Chinese and Western medicine is remarkable.

METHODS AND ANALYSIS

We will search for PubMed, Cochrane Library, AMED, EMbase, WorldSciNet; Nature, Science online and China Journal Full-text Database (CNKI), China Biomedical Literature CD-ROM Database (CBM), and related randomized controlled trials included in the China Resources Database. The time is limited from the construction of the library to November 2018. We will use the criteria provided by Cochrane 5.1.0 for quality assessment and risk assessment of the included studies, and use the Revman 5.3 and Stata13.0 software for meta-analysis of the effectiveness, recurrence rate, and symptom scores of ureteral.

ETHICS AND DISSEMINATION

This systematic review will evaluate the efficacy and safety of Traditional Chinese medicine for ureteral. Because all of the data used in this systematic review and meta-analysis has been published, this review does not require ethical approval. Furthermore, all data will be analyzed anonymously during the review process Trial.Registration number: PROSPERO CRD42019137095.

The usefulness of the maximum Hounsfield units (HU) in predicting the shockwave lithotripsy outcome for ureteral stones and the proposal of novel indicators using the maximum HU

Computed tomography (CT) attenuation value of ureteral stones is one of the predictors of shockwave lithotripsy (SWL) outcome. It is common to use the mean Hounsfield units (HU) to describe the CT attenuation value. However, an observer bias can occur when measuring the mean HU in the conventional method. On the other hand, our way to obtain only the maximum HU is simpler and less biased. We retrospectively evaluated 464 patients with ureteral stones who underwent SWL and compared predictive accuracy of various factors including maximum and mean HU. Results were determined after a single SWL. Predictors of SWL success were examined by the statistical analysis of successful and failed groups. 324 of the 464 patients who underwent SWL were stone-free after a single SWL. Significant differences were found in factors related to CT attenuation value and stone size. As a result of receiver operating characteristic analysis, it was found that maximum HU and mean HU, major diameter and volume have equivalent prediction accuracy, respectively. Multivariate analysis revealed that maximum HU and major diameter were included in independent predictors. We also examined the new original indicators using maximum HU and major diameter. Stone-resistant probability obtained from the logistic model and Maximum HU and Major diameter Index obtained by multiplying maximum HU by major diameter were useful for predicting SWL success, respectively. In conclusion, maximum HU and mean HU have equivalent predictive accuracy, and maximum HU is easier to measure and less biased than mean HU.

Quantitative factors of unenhanced CT for predicting fragmenting efficacy of extracorporeal shock wave lithotripsy on pancreatic duct stones

AIM

To find potential predictors at unenhanced computed tomography (CT) to evaluate the stone clearance rate (SCR) of pancreatic duct stones (PDS) by extracorporeal shock wave lithotripsy (ESWL).

MATERIALS AND METHODS

A total of 106 consecutive patients with multiple stones of maximal transverse length (MTL) 0.3-3.6 cm in pancreatic head region were enrolled. Unenhanced CT was performed both before and after ESWL therapy. All patients underwent ESWL therapy using an electromagnetic lithotripter, and were divided into three groups with threshold values of >90%, 50-90% and <50% of SCR. Factors including sex, age, MTL, mean stone density (MSD), standard deviation of stone density (SDSD), variation coefficient of stone density (VCSD), attenuation density (AD), pancreatic duct diameter (PDD), skin-to-stone distance (SSD), and number of ESWL sessions were analysed, and their impact on SCR was evaluated by univariate and multivariate analyses. The diagnostic threshold of the independent predicting index was further calculated, and its relationship with SCR and ESWL was analysed by drawing a trend line.

RESULTS

The only significant predictor of SCR by ESWL was MSD (p<0.05). More courses of ESWL therapy are required in PDS patients with an MSD higher than the cut-off value of 375.4 HU (mean, 2.6 versus 1.8), but with a relatively lower SCR (mean, 43.96 versus 88.68%) .

CONCLUSION

MSD on pre-treatment unenhanced CT is an independent predictor for SCR of PDS patients treated with ESWL. Using a cut-off value of 375.4 HU, patients with low MSD are good candidates for ESWL therapy.

Predictors of Uric Acid Stones: Mean Stone Density, Stone Heterogeneity Index, and Variation Coefficient of Stone Density by Single-Energy Non-Contrast Computed Tomography and Urinary pH

We analyzed the capacities of pertinent parameters (determined by single-energy non-contrast computed tomography [NCCT]) and urinary pH to predict uric acid stones. We reviewed the medical records of 501 patients whose stones were removed surgically or passed spontaneously between December 2014 and April 2016. Qualifying participants (n = 420) were stratified by the nature of the stone (calcium oxalate, uric acid, or infectious). Based on NCCT, we determined maximal stone length (MSL), mean stone density (MSD), and stone heterogeneity index (SHI) using Hounsfield units (HU) and calculated the variant coefficient of stone density (VCSD = SHI/MSD × 100). Urinary pH was also ascertained. Mean patient age was 55.55 ± 15.46 years. MSD (448.59 ± 173.21 HU), SHI (100.81 ± 77.37 HU), and VCSD (22.58 ± 10.55) proved to be significantly lower in uric acid versus other types of stones, as did urinary pH (5.33 ± 0.56; all p < 0.001). Receiver operating characteristic (ROC) curves depicting predictability of uric acid stones yielded area under ROC curve (AUC) values for MSD, SHI, VCSD, and urinary pH of 0.806 (95% CI: 0.761⁻0.850), 0.893 (95% CI: 0.855⁻0.931), 0.782 (95% CI: 0.726⁻0.839), and 0.797 (95% CI: 0.749⁻0.846), respectively, with corresponding cutpoints of 572.3 HU, 140.4 HU, 25.79, and 6.0. Among these four parameters, SHI was verifiably (DeLong's test) the most effective predictor of uric acid stones (all p < 0.001). Compared with MSD, VCSD, and urinary pH, SHI may better predict uric acid stones, using a cutpoint of 140.4 HU.

Noncontrast Computed Tomography Parameters for Predicting Shock Wave Lithotripsy Outcome in Upper Urinary Tract Stone Cases

Kidney stones are a major public health concern with continuously increasing worldwide prevalence. Shock wave lithotripsy (SWL) is the first line treatment choice for upper urinary tract calculi with ureteroscopy and has advantages of safety and noninvasiveness, but the treatment success rate of SWL is lower than that of other therapies. It is therefore important to identify predictive factors for SWL outcome and select a suitable treatment choice for patients with upper urinary tract calculi. In recent years, computed tomography (CT) has become the gold standard for diagnosis of upper urinary tract calculi. Several factors based on CT images, including skin-to-stone distance, mean stone density, stone heterogeneity index, and variation coefficient of stone density, have been reported to be useful for predicting SWL outcome. In addition, a new method of analysis, CT texture analysis, is reportedly useful for predicting SWL outcomes. This review aims to summarize CT parameters for predicting the outcome of shock wave lithotripsy in stone cases in the upper urinary tract.

Indications and contraindications for shock wave lithotripsy and how to improve outcomes

For over 35 years shock wave lithotripsy has proven to be an effective, safe and truly minimally invasive option for the treatment of nephrolithiasis. Various technical factors as well as patient selection can impact the success of the procedure. We used published work focusing on outcomes of shock wave lithotripsy, risk of complications, and strategies for improving stone fragmentation to create this review. Multiple patient and technical factors have been found to impact success of treatment. Skin to stone distance, stone density and composition, size and location of the stone within the urinary system all influence stone free rates. A slower rate with a gradual increasing voltage, precise targeting, proper coupling will improve stone fragmentation and decrease risk of complications. The selection of appropriate patients through a shared decision making process and attention to the technical factors that improve stone free rates is key to providing an effective treatment and patient satisfaction.

Advanced non-contrasted computed tomography post-processing by CT-Calculometry (CT-CM) outperforms established predictors for the outcome of shock wave lithotripsy

OBJECTIVES

To evaluate the predictive value of advanced non-contrasted computed tomography (NCCT) post-processing using novel CT-calculometry (CT-CM) parameters compared to established predictors of success of shock wave lithotripsy (SWL) for urinary calculi.

MATERIALS AND METHODS

NCCT post-processing was retrospectively performed in 312 patients suffering from upper tract urinary calculi who were treated by SWL. Established predictors such as skin to stone distance, body mass index, stone diameter or mean stone attenuation values were assessed. Precise stone size and shape metrics, 3-D greyscale measurements and homogeneity parameters such as skewness and kurtosis, were analysed using CT-CM. Predictive values for SWL outcome were analysed using logistic regression and receiver operating characteristics (ROC) statistics.

RESULTS

Overall success rate (stone disintegration and no re-intervention needed) of SWL was 59% (184 patients). CT-CM metrics mainly outperformed established predictors. According to ROC analyses, stone volume and surface area performed better than established stone diameter, mean 3D attenuation value was a stronger predictor than established mean attenuation value, and parameters skewness and kurtosis performed better than recently emerged variation coefficient of stone density. Moreover, prediction of SWL outcome with 80% probability to be correct would be possible in a clearly higher number of patients (up to fivefold) using CT-CM-derived parameters.

CONCLUSIONS

Advanced NCCT post-processing by CT-CM provides novel parameters that seem to outperform established predictors of SWL response. Implementation of these parameters into clinical routine might reduce SWL failure rates.