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Development and validation of a nomogram to predict impacted ureteral stones via machine learning
Yuanjiong Qi1, Shushuai Yang1, Jingxian Li1
1Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Minerva Urology and Nephrology
|August 2, 2024
Summary
This study developed a nomogram to predict impacted ureteral stones using clinical features. The tool demonstrated high accuracy in predicting stone impaction, aiding in preoperative diagnosis and treatment planning.
Area of Science:
- Urology
- Medical Imaging
- Predictive Modeling
Background:
- Impacted ureteral stones pose diagnostic challenges.
- Accurate preoperative prediction is crucial for effective treatment planning.
Purpose of the Study:
- To develop and validate a nomogram for predicting impacted ureteral stones.
- Utilize simple, readily available clinical features for prediction.
Main Methods:
- Retrospective analysis of 480 patients undergoing ureteroscopic lithotripsy (URSL).
- Development of a nomogram using Lasso and multivariate logistic regression.
- Validation using training, validation, and test datasets. Performance assessed via ROC curves, calibration curves, and DCA.
Main Results:
- Key predictors identified: flank pain, hydronephrosis, stone dimensions, Hounsfield units (HU) below stone, HU ratio, and ureteral wall thickness (UWT).
- Nomogram achieved high predictive performance with AUCs of 0.907 (training) and 0.874 (validation).
- Excellent clinical applicability and performance confirmed across datasets.
Conclusions:
- A validated nomogram for preoperative diagnosis of impacted ureteral stones was established.
- This tool significantly aids in the management and treatment of impacted ureteral stones.
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