Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Abstract

Abstract The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL), and compare its performance to the S.T.O.N.E. and Guy’s stone scores. This is a retrospective study that included 320 PCNL patients. The pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each MLM, were mean bootstrap estimate with CI, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC. Out of the 320 patients who underwent PCNL the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65–0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63–0.85], 0.759, 0.72, and 0.769 respectively. The SVM results were 0.70 [0.60–0.79], 0.725, 0.74, and 0.751 respectively. The AUC of Guy’s stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81–0.92], an accuracy of 0.70, and an AUC of 0.795. While the XGBoost results were 0.84 [0.78–0.91], 0.74, and 0.84 respectively. The SVM results were 0.86 [0.80–0.91], 0.79, and 0.858 respectively. MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLM systems we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E score.