Single extracorporeal shock-wave lithotripsy for proximal ureter stones: Can CT texture analysis technique help predict the therapeutic effect?

The Association Between Stone Heterogenicity and the Success of Shock Wave Lithotripsy for High Density Upper Urinary Tract Stones: A Muti-Center Prospective Cohort

OBJECTIVE

To prospectively investigate the predictive value of radiological stone heterogenicity for the success of shock-wave lithotripsy (SWL) in patients with high-density upper urinary tract stones.

METHODS

This multi-center prospective study was conducted from June 2020 to December 2023. The radiological stone density index measured by computed tomography (CT) included the mean, standard deviation (SD), and coefficient variation (CV) of the Hounsfield Unit value of stones. The success was defined as no evidence of clinically significant stone fragments (≥4 mm) on a plain X-ray and ultrasound of the upper urinary tract 1 month after SWL. Binary logistic regression models were used to assess the odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Finally, 533 patients with high-density upper urinary tract stones were enrolled in this study. There were 68.5% (379/533) patients experienced success after SWL treatment. After adjusted confounding variables, compared with the patients in the lowest quartile of stone density index, the odds ratios (95% CI) for the extreme quartile was 1.42 (0.71, 2.81, P for trend = .443), .03 (.01, .09, P for trend <.001), and .02 (.01, .07, P for trend <.001) for MSD, SD, and CV, respectively. Furthermore, this linear association was confirmed only for SD and CV regardless of gender, body mass index, stone size, and stone location.

CONCLUSION

This multi-center prospective study demonstrated a positive association between the stone heterogenicity and the success of SWL in patients with high-density upper urinary tract stones regardless of gender, body mass index, stone size, and stone location. However, this association was not observed in mean stone density.

Which Measure of Stone Burden is the Best Predictor of Interventional Outcomes in Urolithiasis: A Systematic Review and Meta-analysis by the YAU Urolithiasis Working Group and EAU Urolithiasis Guidelines Panel

Background and objective

Stone size has traditionally been measured in one dimension. This is reflected in most of the literature and in the EAU guidelines. However, recent studies have shown that multidimensional measures provide better prediction of outcomes.

Methods

We performed a systematic review and meta-analysis of the prognostic accuracy of measures of stone size (PROSPERO reference CRD42022346967). We considered all studies reporting prognostic accuracy statistics on any intervention for kidney stones (extracorporeal shockwave lithotripsy [ESWL], ureterorenoscopy [URS], or percutaneous nephrolithotomy [PCNL]; Population) using multiplane measurements of stone burden (area in mm2 or volume in mm3; Intervention) in comparison to single-plane measurements of stone burden (size in mm; Intervention) for the study-defined stone-free rate (Outcome) in a PICO-framed question. We also assessed complication rates (overall and by Clavien-Dindo grade) and the operative time as secondary outcomes. Searches were made between 1970 and August 2023. We used the DeLong method to compare receiver operating characteristic (ROC) curves.

Key findings and limitations

Of 24 studies included in the review, 12 were eligible for comparative analysis with the DeLong test following meta-analysis of prognostic accuracy. For prediction of stone-free status, the area under the ROC curve (AUC) was significantly higher for stone volume than for stone size (0.71 vs 0.67; p < 0.001). Subanalyses confirmed this for ESWL and URS, but not for PCNL. For URS, the AUC was also significantly higher for stone area than for stone size (0.79 vs 0.77; p < 0.001). Throughout all analyses, there was no difference in AUC between stone area and stone volume. There was high risk of bias for all analyses apart from the URS subanalyses.

Conclusions and clinical implications

According to the limited data currently available, stone-free rates are predicted with significantly higher accuracy using multidimensional measures of stone burden in comparison to a single linear measurement.

Patient summary

We reviewed different ways of measuring the size of stones in the kidney or urinary tract and compared their accuracy in predicting stone-free rates after treatment. We found that measurement of the stone area (2 dimensions) or stone volume (3 dimensions) is better than stone diameter (1 dimension) in predicting stone-free status after treatment.

CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm

OBJECTIVES

To develop a deep learning (DL) model based on computed tomography (CT) images to predict the success of extracorporeal shock wave lithotripsy (SWL) treatment for patients with ureteral stones larger than 1 cm.

MATERIALS AND METHODS

We enrolled 333 patients who underwent SWL treatment for ureteral stones and randomly divided them into training and test sets. A DL model was built based on CT images of ureteral stones to predict SWL outcomes. The predictive efficacy of the DL model was assessed by comparing it with traditional and radiomics models.

RESULTS

The DL model demonstrated significantly better predictive performance in both training and test sets compared to radiomics (training set, AUC: 0.993 vs. 0.923, P < 0.001; test set AUC: 0.982 vs. 0.846, P < 0.001) and traditional models (training set AUC: 0.993 vs. 0.75, P = 0.005; test set AUC: 0.982 vs. 0.677, P < 0.001). Decision curve analysis (DCA) also proved that the DL model brought more benefit in predicting the success of SWL treatment than other methods.

CONCLUSION

The DL model based on CT images showed excellent ability to predict the probability of success of SWL treatment for patients with ureteral stones larger than 1 cm, providing a new auxiliary tool for clinical treatment decision-making.

CT-based AI model for predicting therapeutic outcomes in ureteral stones after single extracorporeal shock wave lithotripsy through a cohort study

OBJECTIVES

Exploring the efficacy of an artificial intelligence (AI) model derived from the analysis of computed tomography (CT) images to precisely forecast the therapeutic outcomes of singular-session extracorporeal shock wave lithotripsy (ESWL) in the management of ureteral stones.

METHODS

A total of 317 patients diagnosed clinically with ureteral stones were included in this investigation. Unenhanced CT was administered to the participants within the initial fortnight preceding the inaugural ESWL. The internal cohort consisted of 250 individuals from a local healthcare facility, whereas the external cohort comprised 67 participants from another local medical institution. The proposed framework comprises three main components: an automated semantic segmentation model developed using 3D U-Net, a feature extractor that integrates radiomics and autoencoder techniques, and an ESWL efficacy prediction model trained with various machine learning algorithms. All participants underwent thorough postoperative follow-up examinations 4 weeks hence. The efficacy of ESWL was defined by the absence of stones or residual fragments measuring ≤2 mm in KUB X-ray assessments. Model stability and generalizability were judiciously validated through a fivefold cross-validation approach and a multicenter external test strategy. Moreover, Shapley Additive Explanations (SHAP) values for individual features were computed to elucidate the nuanced contributions of each feature to the model's decision-making process.

RESULTS

The semantic segmentation model the authors constructed exhibited an average Dice coefficient of 0.88±0.08 on the external testing set. ESWL classifiers built using Support Vector Machine (SVM), Random Forest (RF), XGBoost (XB), and CatBoost (CB) achieved AUROC values of 0.78, 0.84, 0.85, and 0.90, respectively, on the internal validation set. For the external testing set, SVM, RF, XB, and CB predicted ESWL with AUROC values of 0.68, 0.79, 0.80, and 0.83, respectively, with the last one being the optimal algorithm. The radiomics features and auto-encoder features made significant contributions to the decision-making process of the classification model.

CONCLUSIONS

This investigation unmistakably underscores the remarkable predictive prowess exhibited by a scrupulously crafted AI model using CT images to precisely anticipate the therapeutic results of a singular session of ESWL for ureteral stones.

Role of Noncontrast Computed Tomography Parameters in Predicting the Outcome of Extracorporeal Shock Wave Lithotripsy for Upper Urinary Stones Cases: A Meta-analysis

RATIONALE

Extracorporeal shock wave lithotripsy (ESWL) is widely considered the primary approach for managing urinary tract stones. This study aimed to assess the predictive factors associated with non-contrast computed tomography (NCCT)-based parameters of upper urinary stones in relation to the outcomes of ESWL.

MATERIALS AND METHODS

A systematic search was conducted in PubMed, ScienceDirect, Web of Science, and Cochrane Library to identify all relevant studies published up to June 3, 2023. Several NCCT-based parameters to predict ESWL outcomes, comprised of mean stone density (MSD), skin-to-stone distance (SSD), and stone size, were extracted and analyzed using Review Manager software.

RESULTS

Out of 979 publications screened, a total of 39 publications, involving 7869 patients, were enrolled in the analysis. The pooled estimate demonstrated significant differences between MSD, and stone size between successful and failure of stone fragmentation groups, in which lower values of these parameters are associated with successful ESWL outcomes.

CONCLUSION

The results from the current study suggested that lower NCCT parameters, notably MSD, SSD, and stone size, are significantly associated with successful ESWL outcome. However, additional large-scale prospective studies are required to utilize these parameters effectively, and the optimal cutoff value should be determined.

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.

Can shear wave elastography predict the success of shock‑wave lithotripsy used in renal stones treatment? A prospective study

OBJECTIVE

To evaluate the usage of shear wave elastography (SWE) in the prediction of the success rate of shock‑wave lithotripsy (SWL) treatment of renal stones.

PATIENTS AND METHODS

In the present study, SWL was performed for 100 patients presented with renal stones in the duration from May 2022 to August 2023. The patients were divided into 2 groups SWL responders and non-responders. The study compared between the 2 groups in terms of baseline parameters of the patients as age, sex, body mass index (BMI), stone size, stone location, stone density (HU), skin-to-stone distance (SSD), the degree of hydronephrosis and the stone elastography values.

RESULTS

There was no statistically significant relation between stone-free rate and degree of obstruction (p = 0.628), stone size (p = 0.390) upper calyceal location (p = 0.17), middle calyceal location (p = 0.66), and renal pelvis location (p = 1.0). Nevertheless, a statistically significant relation was found as regards lower calyceal location, stone density (HU), and stone Elastography values using multivariate analysis.

CONCLUSIONS

Measurement of stone density by shear wave elastography (SWE) can be used as an alternative to HU in decision-making before SWL. SWL success depends mainly on stone site, HU, and SWE values.

Non-contrast computed tomography-based factors in predicting ESWL success: A systematic review and meta-analysis

PURPOSE

We assessed the efficacy of some predictive factors that can be measured with non-contrast computed tomography and may affect ESWL success with a systematic review and meta-analysis.

MATERIALS AND METHODS

All data sources were broadly investigated up to April 2022. Data were extracted from the relevant studies and analyzed with RevMan software. In a random effects model, standard mean difference (SMD) and risk ratio (RR) values were given with 95% confidence intervals.

RESULTS

In total, pooled analysis included 7148 patients in 43 studies. The combined effect estimate showed significant differences between the ESWL success and ESWL failure groups in terms of Hounsfield unit (HU), Hounsfield density (HD), skin to stone distance (SSD), ureteral wall thickness (UWT), stone volume, stone area, abdominal fat parameters, diameter of proximal ureter, and hydronephrosis. However, perinephric stranding and renal cortical thickness were not found to be statistically significant between the study groups.

CONCLUSIONS

HU, HD, SSD, UWT, stone volume, stone area, abdominal fat parameters, diameter of proximal ureter and hydronephrosis are effective factors for prediction of ESWL success. It is important to decide on treatment before the procedure for stones with appropriate diameter for ESWL.

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith

OBJECTIVE

To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith.

METHOD

Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model.

RESULTS

The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model.

CONCLUSIONS

The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.

Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

OBJECTIVES

To explore the utility of radiomics and deep learning model in assessing the risk factors for sepsis after flexible ureteroscopy lithotripsy (FURL) or percutaneous nephrolithotomy (PCNL) in patients with ureteral calculi.

METHODS

This retrospective analysis included 847 patients with treatment-naive proximal ureteral calculi who received FURL or PCNL. All participants were preoperatively conducted non-contrast computed tomography scans, and relevant clinical information was meanwhile collected. After propensity score matching, the radiomics model was established to predict the onset of sepsis. A deep learning model was also adapted to further improve the prediction accuracy. Performance of these trained models was verified in another independent external validation set including 40 cases of ureteral calculi patients.

RESULTS

The overall incidence of sepsis after FURL or PCNL was 5.9%. The least absolute shrinkage and selection operator (LASSO) regression analysis revealed 26 predictive variables, with an overall AUC of 0.881 (95% CI, 0.813-0.931) and an AUC of 0.783 (95% CI, 0.766-0.801) in external validation cohort. Judicious adaption of a deep neural network (DNN) model to our dataset improved the AUC to 0.920 (95% CI, 0.906-0.933) in the internal validation. To eliminate the overfitting, external validation was carried out for DNN model (AUC = 0.874 (95% CI, 0.858-0.891)).

CONCLUSIONS

The DNN was more effective than the LASSO model in revealing risk factors for sepsis after FURL or PCNL in single ureteral calculi patients, and females are more susceptible to sepsis than males. Deep learning models have the potential to act as gatekeepers to facilitate patient stratification.

KEY POINTS

• Both the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models were shown to be effective in sepsis prediction. • The DNN model achieved superior prediction capability, with an AUC of 0.920 (95% CI, 0.906-0.933). • DNN-assisted model has potential to serve as a gatekeeper to facilitate patient stratification.

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.

Usability of shear wave elastography to predict the success of extracorporeal shock-wave lithotripsy: prospective pilot study

The present study is intended to investigate the usability of shear wave elastography (SWE) in predicting the success of extracorporeal shock-wave lithotripsy (ESWL) used in kidney stone treatment. ESWL was performed on a total number of 52 patients diagnosed with kidney stones between May 2019 and July 2020. The presence of a residual stone greater than 4 mm was accepted as failure. The patients were divided into two groups as ESWL success and ESWL failure. SWE and Hounsfield unit (HU) measurements of stones were performed in all patients before ESWL. The two groups were compared in terms of age, gender, stone localisation, stone size, body mass index (BMI), skin-to-stone distance, HU, and SWE values of the stones. ESWL was successful in 30 of the 52 patients included in the study, while it failed in 22 of them. While the mean SWE value was 7.3 (7.9 ± 2.2) kPa in patients with success in ESWL, it was 14.6 (17.9 ± 10.2) kPa in those with failed ESWL. The mean HU was 883.5 (841.4 ± 191.1) in patients with success in ESWL and 1078 (1115.5 ± 183) in those with failed ESWL. Both SWE and HU values of the stones were found to be statistically significantly lower in the successful group (p < 0.05). It was seen that SWE and HU values were correlated to each other. The groups of successful and failed ESWL did not differ significantly in terms of age, gender distribution, stone size, BMI, and skin-to-stone distance (p > 0.05). With SWE, the hardness of the stone can be measured and its suitability for ESWL can be evaluated. It can be used as an alternative parameter to HU before ESWL treatment, since it has a lower cost compared to computed tomography (CT) and does not contain radiation.

A Novel Clinical-Radiomics Model Pre-operatively Predicted the Stone-Free Rate of Flexible Ureteroscopy Strategy in Kidney Stone Patients

Purpose: The purpose of the study is to develop and validate a novel clinical-radiomics nomogram model for pre-operatively predicting the stone-free rate of flexible ureteroscopy (fURS) in kidney stone patients. Patients and Methods: Altogether, 2,129 fURS cases with kidney stones were retrospectively analyzed, and 264 patients with a solitary kidney stone were included in a further study. For lower calyx calculi, a radiomics model was generated in a primary cohort of 99 patients who underwent non-contrast-enhanced computed tomography (NCCT). Radiomics feature selection and signature building were conducted by using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was employed to build a model incorporating radiomics and potential clinical factors. Model performance was evaluated by its discrimination, calibration, and clinical utility. The model was internally validated in 43 patients. Results: The overall success rate of fURS was 72%, while the stone-free rate (SFR) for lower calyx calculi and non-lower calyx calculi was 56.3 and 90.16%, respectively. On multivariate logistic regression analysis of the primary cohort, independent predictors for SFR were radiomics signature, stone volume, operator experience, and hydronephrosis level, which were all selected into the nomogram. The area under the curve (AUC) of clinical-radiomics model was 0.949 and 0.947 in the primary and validation cohorts, respectively. Moreover, the calibration curve showed a satisfactory predictive accuracy, and the decision curve analysis indicated that the nomogram has superior clinical application value. Conclusion: In this novel clinical-radiomics model, the radiomics scores, stone volume, hydronephrosis level, and operator experience were crucial for the flexible ureteroscopy strategy.

Ultraslow full-power shock wave lithotripsy protocol in the management of high attenuation value upper ureteric stones: A randomized comparative study

OBJECTIVES

To evaluate the efficacy and safety of ultraslow full-power shock wave lithotripsy protocol in the management of high attenuation value upper ureteric stones compared with slow-rate, power-ramping shock wave lithotripsy.

METHODS

This was a randomized trial enrolling patients with a single high attenuation value (≥1000 HU) upper ureteric stones between January 2019 and July 2019. Ultraslow full-power shock wave lithotripsy (54 patients) was applied at a rate of 30 shock waves/min with power ramping from 6 to 18 kV for 100 shock waves, then a safety pause for 2 min, followed by ramping 18-22 kV for 100 shock waves, then a safety pause for 2 min. Then, full power (22 kV) was maintained until the end of the session. Slow-rate, power-ramping shock wave lithotripsy (47 patients) was applied at a rate of 60 shock waves/min with power ramping from 6 to 10 kV during the first 500 shock waves, then from 11 to 22 kV during the next 1000 shock waves, then maintained on 22 kV in the last 1500 shock waves. Up to three sessions were carried out with a follow up 3 months after the last session. The primary outcome was the stone-free rate. Perioperative data of the two protocols were compared.

RESULTS

There was no significant difference in preoperative data. The stone-free rate was significantly higher in ultraslow full-power shock wave lithotripsy after single (92.6% vs 23.4%) and multiple (96.3% vs 63.8%) sessions. Most complications were mild, with no significant difference between both groups (9.3% vs 12.8%; P = 0.573). Logistic regression analysis identified ultraslow full-power shock wave lithotripsy protocol as the only significant independent factor for the stone-free rate (odds ratio 12.589, P = 0.025).

CONCLUSION

Ultraslow full-power shock wave lithotripsy for high attenuation value upper ureteric stones is associated with a significantly higher stone-free rate, and with mild complications that are comparable to those of standard shock wave lithotripsy.

Factors affecting success in the treatment of proximal ureteral stones larger than 1 cm with extracorporeal shockwave lithotripsy in adult patients

The aim of the study was to analyze the factors predicting success for the treatment of proximal ureteral stones larger than 1 cm with extracorporeal shockwave lithotripsy (SWL) in adult patients. Between January 2014 and December 2018, 480 adult patients in total underwent SWL and data were retrospectively evaluated. Patients with multiple ureteral stones, solitary kidney, congenital abnormality, skeletal system abnormality, history of previous urinary system surgery and SWL, renal insufficiency, patients who could not tolerate SWL and pediatric patients were excluded from the study. The remaining 415 patients were divided into two groups as success (Group 1, n = 307) and failure (Group 2, n = 108). The overall success rate was 73.9%. The values of stone size, Hounsfield Unit (HU), skin to stone distance (SSD), ureteral wall thickness (UWT), proximal ureter diameter, renal pelvis diameter, hydronephrosis grade, and duration of renal colic were significantly higher in Group 2 compared to Group 1. In binary logistic regression analyses, HU, UWT, and proximal ureter diameter were found to be independent predictors. HU, UWT, and proximal ureter diameter had sensitivity and specificity of 92-92%, 88-23%, and 87-46%, with cutoff values of 740 HU, 2.5 mm and 8.5 mm, respectively. The area under the curve values were 0.96, 0.97, and 0.96 for HU, UWT, and proximal ureter diameter, respectively. The CT-based parameters, including HU, UWT, and proximal ureter diameter are independent predictive factors with excellent accuracy for the treatment of proximal ureteral stones larger than 1 cm with SWL in adult patients. Based on these factors, SWL can be considered for proximal ureteral stones larger than 1 cm.

Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables

We aimed to develop and evaluate a statistical model, which included known pre-treatment factors and new computed tomography texture analysis (CTTA) variables, for its ability to predict the likelihood of a successful outcome after extracorporeal shockwave lithotripsy (SWL) treatment for renal and ureteric stones. Up to half of patients undergoing SWL may fail treatment. Better prediction of which cases will likely succeed SWL will help patients to make an informed decision on the most effective treatment modality for their stone. 19 pre-treatment factors for SWL success, including 6 CTTA variables, were collected from 459 SWL cases at a single centre. Univariate and multivariable analyses were performed by independent statisticians to predict the probability of a stone free (both with and without residual fragments) outcome after SWL. A multivariable model had an overall accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome. The variables most frequently chosen for the model were those which represented stone size. Although previous studies have suggested SWL can be reliably predicted using pre-treatment factors and that analysis of CT stone images may improve outcome prediction, the results from this study have not produced a useful model for SWL outcome prediction.

Total Surface Area Influences Stone Free Outcomes in Shock Wave Lithotripsy for Distal Ureteral Calculi

Introduction: Current American Urological Association guidelines recommend ureteroscopy (URS) as primary management of distal ureteral stones and shock wave lithotripsy (SWL) as a secondary option. Utilization of SWL in the management of nephrolithiasis in North America has decreased. We hypothesized that SWL continues to be an effective option in the management of distal ureteral calculi and studied data from our center in patients who received SWL for distal ureteral stones. Methods: A retrospective review was performed of 104 patients treated initially with SWL for distal ureteral calculi between 2011 and 2018 at this institution. The success rate of SWL was assessed through radiologic imaging and if subsequent procedures were required to render patients stone free. Results: Operative note and chart review identified 104 patients who presented with distal ureteral stones and were treated with SWL as the initial form of management. Average patient age was 52.2 ± 15.3 years, average BMI was 27.4 ± 5.7, and average total axial stone surface area was 25.96 ± 14.32 mm². Of these patients, 78.8% (n = 82) were stone free following one SWL and required no subsequent procedures. Of these patients, 87.5% (n = 91) were stone free following a second SWL, and 87.5% (n = 91) were stone free following a secondary URS. After the initial SWL, residual stones were identified in 21.2% of patients (n = 22). Four patients, 3.8%, required a salvage URS following a failed second SWL to achieve stone-free status. Conclusion: One SWL procedure offers a stone-free rate (SFR) of 78.8% and after two SWLs an 87.5% SFR. Only 12.5% of patients undergoing SWL at our center required URS to achieve a stone-free status. SWL is an effective modality in the treatment of distal ureteral stones.