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

Area of Science:

• Urology
• Medical Imaging
• Statistical Modeling

Background:

• Extracorporeal shockwave lithotripsy (SWL) is a common treatment for renal and ureteric stones.
• Treatment failure occurs in up to 50% of patients undergoing SWL.
• Accurate prediction of SWL success is crucial for informed patient decision-making regarding treatment options.

Purpose of the Study:

• To develop and evaluate a statistical model for predicting SWL success.
• To incorporate pre-treatment factors and computed tomography texture analysis (CTTA) variables into the predictive model.
• To improve the prediction of stone-free outcomes after SWL.

Main Methods:

• A cohort of 459 patients undergoing SWL for renal and ureteric stones was analyzed.
• Nineteen pre-treatment factors, including 6 CTTA variables, were collected.
• Univariate and multivariable analyses were performed to predict successful SWL outcomes.

Main Results:

• A multivariable model achieved 66% accuracy in predicting successful SWL outcomes via Receiver Operator Curve (ROC) analysis.
• Stone size was the most frequently selected variable in the predictive model.
• The developed model did not prove sufficiently useful for predicting SWL outcomes.

Conclusions:

• Despite incorporating CTTA, the developed statistical model failed to provide a useful prediction of SWL success.
• Further research is needed to identify reliable predictors for SWL outcomes.
• Current pre-treatment factors and CTTA variables are insufficient for accurate SWL success prediction.