Yang SW. et al., 2020: Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy

Monday, 08 February 2021

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Abstract

Background: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods.

Methods: We retrospectively reviewed the medical records of 358 patients who underwent SWL for urinary stone (kidney and upper-ureter stone) between 2015 and 2018 and evaluated the possible prognostic features, including patient population characteristics, urinary stone characteristics on a non-contrast, computed tomographic image. We performed 80% training set and 20% test set for the predictions of success and mainly used decision tree-based machine learning algorithms, such as random forest (RF), extreme gradient boosting trees (XGBoost), and light gradient boosting method (LightGBM).

Results: In machine learning analysis, the prediction accuracies for stone-free were 86.0, 87.5, and 87.9%, and those for one-session success were 78.0, 77.4, and 77.0% using RF, XGBoost, and LightGBM, respectively. In predictions for stone-free, LightGBM yielded the best accuracy and RF yielded the best one in those for one-session success among those methods. The sensitivity and specificity values for machine learning analytics are (0.74 to 0.78 and 0.92 to 0.93) for stone-free and (0.79 to 0.81 and 0.74 to 0.75) for one-session success, respectively. The area under curve (AUC) values for machine learning analytics are (0.84 to 0.85) for stone-free and (0.77 to 0.78) for one-session success and their 95% confidence intervals (CIs) are (0.730 to 0.933) and (0.673 to 0.866) in average of methods, respectively.

Conclusions: We applied a selected machine learning analysis to predict the result after treatment of SWL for urinary stone. About 88% accurate machine learning based predictive model was evaluated. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for SWL success outcomes.
BMC Urol. 2020 Jul 3;20(1):88. doi: 10.1186/s12894-020-00662-x. PMID: 32620102. FREE ARTICLE

Comments 1

Peter Alken on Monday, 08 February 2021 09:30

Interesting but either difficult to understand or to believe. Eleven authors are Urologists and three are from the Division of Medical Mathematics. I am not sure about the value of this publication but I am only one person.

42 different parameters were used to feed the machine learning algorithms. Among them were
• 23 lab values like creatinine or cholesterol in blood (15) and like bilirubin or ketone in urine (8), to mention a few, and
• 6 stone parameters like density or volume and
• 5 body parameters like skin to stone distances or psoas muscle cross sectional area, all derived from CT data.

No data on ESWL parameters applied in the different cases nor data on the stone position within the collecting system were used. “… to overcome the relatively small number of data, we collected SWL treatment data for both the upper ureter and kidney stones without their positional information. … Even though the positional information for urinary stones is neglected, the predictive model can catch the effective accuracy in predictions.”

Like this the possible effect of the cholesterol value in blood on ESWL success was evaluated in the machine learning process while the effect of a stone position in the lower calyx was not.

“Mean stone density (MSD) was the most significant in feature importance, and
stone volume, skin to stone distance (SSD) and stone length were the next most closely associated with stone-free prediction of SWL treatment outcomes in patients with urinary stone. In addition, stone volume was the most significant in feature importance, and MSD, stone length, SSD and psoas muscle cross sectional area were the next most closely associated with the one-session success prediction in this study. Thus, these would be clinically useful parameters in order.”
“The major contribution of this study was to enable urologists to choose patients who would realize the most optimal results from SWL. After prediction analysis, patients who have a high risk of stone-free failure can select another method….
Each outcome in the predictive analysis exceeded 85% for stone-free and 77% for one-session success, especially, LightGBM and XGBoost showed good prediction
outcomes of more than 87% in stone-free prediction.”

Like this without knowing the position of the stone in the ureter or upper or lower calyx the success rate for a given stone with similar parameters in one of these two areas will be identical!!???

Peter Alken

42 different parameters were used to feed the machine learning algorithms. Among them were 
•	23 lab values like creatinine or cholesterol in blood (15) and like bilirubin or ketone in urine (8), to mention a few, and 
•	6 stone parameters like density or volume and
•	5 body parameters like skin to stone distances or psoas muscle cross sectional area, all derived from CT data.

No data on ESWL parameters applied in the different cases nor data on the stone position within the collecting system were used.  “… to overcome the relatively small number of data, we collected SWL treatment data for both the upper ureter and kidney stones without their positional information. … Even though the positional information for urinary stones is neglected, the predictive model can catch the effective accuracy in predictions.”

Like this the possible effect of the cholesterol value in blood on ESWL success was evaluated in the machine learning process while the effect of a stone position in the lower calyx was not.

“Mean stone density (MSD) was the most significant in feature importance, and
stone volume, skin to stone distance (SSD) and stone length were the next most closely associated with stone-free prediction of SWL treatment outcomes in patients with urinary stone. In addition, stone volume was the most significant in feature importance, and MSD, stone length, SSD and psoas muscle cross sectional area were the next most closely associated with the one-session success prediction in this study. Thus, these would be clinically useful parameters in order.”
“The major contribution of this study was to enable urologists to choose patients who would realize the most optimal results from SWL. After prediction analysis, patients who have a high risk of stone-free failure can select another method….
Each outcome in the predictive analysis exceeded 85% for stone-free and 77% for one-session success, especially, LightGBM and XGBoost showed good prediction
outcomes of more than 87% in stone-free prediction.”

Like this without knowing the position of the stone in the ureter or upper or lower calyx the success rate for a given stone with similar parameters in one of these two areas will be identical!!???

Peter Alken

Interesting but either difficult to understand or to believe. Eleven authors are Urologists and three are from the Division of Medical Mathematics. I am not sure about the value of this publication but I am only one person. 42 different parameters were used to feed the machine learning algorithms. Among them were • 23 lab values like creatinine or cholesterol in blood (15) and like bilirubin or ketone in urine (8), to mention a few, and • 6 stone parameters like density or volume and • 5 body parameters like skin to stone distances or psoas muscle cross sectional area, all derived from CT data. No data on ESWL parameters applied in the different cases nor data on the stone position within the collecting system were used. “… to overcome the relatively small number of data, we collected SWL treatment data for both the upper ureter and kidney stones without their positional information. … Even though the positional information for urinary stones is neglected, the predictive model can catch the effective accuracy in predictions.” Like this the possible effect of the cholesterol value in blood on ESWL success was evaluated in the machine learning process while the effect of a stone position in the lower calyx was not. “Mean stone density (MSD) was the most significant in feature importance, and stone volume, skin to stone distance (SSD) and stone length were the next most closely associated with stone-free prediction of SWL treatment outcomes in patients with urinary stone. In addition, stone volume was the most significant in feature importance, and MSD, stone length, SSD and psoas muscle cross sectional area were the next most closely associated with the one-session success prediction in this study. Thus, these would be clinically useful parameters in order.” “The major contribution of this study was to enable urologists to choose patients who would realize the most optimal results from SWL. After prediction analysis, patients who have a high risk of stone-free failure can select another method…. Each outcome in the predictive analysis exceeded 85% for stone-free and 77% for one-session success, especially, LightGBM and XGBoost showed good prediction outcomes of more than 87% in stone-free prediction.” Like this without knowing the position of the stone in the ureter or upper or lower calyx the success rate for a given stone with similar parameters in one of these two areas will be identical!!??? Peter Alken

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