Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones.
- Mannil M Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland.
- von Spiczak J Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland.
- Hermanns T Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland.
- Poyet C Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland.
- Alkadhi H Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland.
- Fankhauser CD Institute of Diagnostic and Interventional Radiology and Department of Urology (TH, CP, CDF), University Hospital Zurich, University of Zurich, Switzerland. Electronic address: christian.fankhauser@usz.ch.
- 2018-04-21
Published in:
- The Journal of urology. - 2018
decision support techniques
kidney calculi
lithotripsy
machine learning
treatment outcome
Adult
Aged
Area Under Curve
Cohort Studies
Female
Follow-Up Studies
Humans
Imaging, Three-Dimensional
Kidney Calculi
Lithotripsy
Machine Learning
Male
Middle Aged
Predictive Value of Tests
Retrospective Studies
Risk Assessment
Severity of Illness Index
Tomography, X-Ray Computed
Treatment Outcome
English
PURPOSE
We sought to determine the predictive value of 3-dimensional texture analysis of computerized tomography images for successful shock wave lithotripsy in patients with kidney stones.
MATERIALS AND METHODS
Patients with preoperative and postoperative computerized tomography, previously untreated kidney stones and a stone diameter of 5 to 20 mm were included in study. A total of 224, 3-dimensional texture analysis features of each kidney stone, including attenuation measured in HU and the clinical variables body mass index, initial stone size and skin to stone distance, were analyzed using 5 commonly used machine learning models. The data set was split in a ratio of 2/3 for model derivation and 1/3 for validation. Machine learning based predictions of shock wave lithotripsy success in the validation cohort were evaluated by calculating sensitivity, specificity and the AUC.
RESULTS
For shock wave lithotripsy success the 3 clinical variables body mass index, initial stone size and skin to stone distance showed an AUC of 0.68, 0.58 and 0.63, respectively. No predictive value was found for HU. A random forest classifier using 3, 3-dimensional texture analysis features had an AUC of 0.79. By combining these 3 features with clinical variables discriminatory accuracy improved further with an AUC of 0.85 for 3-dimensional texture analysis features and skin to stone distance, an AUC of 0.8 for 3-dimensional texture analysis features and body mass index, and an AUC of 0.81 for 3-dimensional texture analysis and stone size.
CONCLUSIONS
This preliminary study indicates that the clinical variables body mass index, initial stone size and skin to stone distance show limited value to predict shock wave lithotripsy success while stone HU values were not predictive. Select 3-dimensional texture analysis features identified by machine learning provided incremental accuracy to predict the success of shock wave lithotripsy.
We sought to determine the predictive value of 3-dimensional texture analysis of computerized tomography images for successful shock wave lithotripsy in patients with kidney stones.
MATERIALS AND METHODS
Patients with preoperative and postoperative computerized tomography, previously untreated kidney stones and a stone diameter of 5 to 20 mm were included in study. A total of 224, 3-dimensional texture analysis features of each kidney stone, including attenuation measured in HU and the clinical variables body mass index, initial stone size and skin to stone distance, were analyzed using 5 commonly used machine learning models. The data set was split in a ratio of 2/3 for model derivation and 1/3 for validation. Machine learning based predictions of shock wave lithotripsy success in the validation cohort were evaluated by calculating sensitivity, specificity and the AUC.
RESULTS
For shock wave lithotripsy success the 3 clinical variables body mass index, initial stone size and skin to stone distance showed an AUC of 0.68, 0.58 and 0.63, respectively. No predictive value was found for HU. A random forest classifier using 3, 3-dimensional texture analysis features had an AUC of 0.79. By combining these 3 features with clinical variables discriminatory accuracy improved further with an AUC of 0.85 for 3-dimensional texture analysis features and skin to stone distance, an AUC of 0.8 for 3-dimensional texture analysis features and body mass index, and an AUC of 0.81 for 3-dimensional texture analysis and stone size.
CONCLUSIONS
This preliminary study indicates that the clinical variables body mass index, initial stone size and skin to stone distance show limited value to predict shock wave lithotripsy success while stone HU values were not predictive. Select 3-dimensional texture analysis features identified by machine learning provided incremental accuracy to predict the success of shock wave lithotripsy.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1016/j.juro.2018.04.059
- PMID 29673945
- Persistent URL
- https://folia.unifr.ch/global/documents/119607
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