Success of extracorporeal shock wave lithotripsy based on CT texture analysis

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

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.

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.

A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy

Background

A model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool.

Objective

In this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians.

Methods

According to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model's predictive efficacy and clinical application using data from two different centers.

Results

The study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients' pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model's clinical utility and illustrated specificities of 0.935 and 0.806, respectively.

Conclusion

We found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.

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.

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.