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Three dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones


Mannil, Manoj; von Spiczak, Jochen; Hermanns, Thomas; Poyet, Cédric; Alkadhi, Hatem; Fankhauser, Christian Daniel (2018). Three dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones. Journal of Urology, 200(4):829-836.

Abstract

PURPOSE To determine the predictive value of three-dimensional texture analysis (3D-TA) in computed tomography (CT) images for successful shock wave lithotripsy (SWL) in patients with kidney stones.
MATERIAL AND METHODS Patients with pre and postoperative CT scans, previously untreated kidney stones and a stone diameter of 5-20 mm were included. A total of 224 3D-TA features of each kidney stone, including the attenuation measured in Hounsfield Units (HU), and the clinical variables body mass index (BMI), initial stone size, and skin-to-stone distance (SSD) were analyzed using five 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 for SWL success in the validation cohort were evaluated calculating sensitivity, specificity, and the area-under-the-curve (AUC).
RESULTS For SWL success the three clinical variables BMI, initial stone size and SSD showed AUCs of 0.68, 0.58 and 0.63 respectively, but no predictive value for HU was found. A RandomForest classifier using three 3D-TA features had an AUC of 0.79. By combining these three 3D-TA features with clinical variables, the discriminatory accuracy improved further with an AUC of 0.85 for 3D-TA features and SSD, an AUC of 0.8 for 3D-TA features and BMI and an AUC of 0.81 for 3D-TA and stone size.
CONCLUSION This preliminary study indicates that the clinical variables BMI, initial stone size and SSD show limited value for predicting SWL success, while the HU values of stones were not predictive. Selected 3D-TA features identified by machine learning provided incremental accuracy for predicting the success to SWL.

Abstract

PURPOSE To determine the predictive value of three-dimensional texture analysis (3D-TA) in computed tomography (CT) images for successful shock wave lithotripsy (SWL) in patients with kidney stones.
MATERIAL AND METHODS Patients with pre and postoperative CT scans, previously untreated kidney stones and a stone diameter of 5-20 mm were included. A total of 224 3D-TA features of each kidney stone, including the attenuation measured in Hounsfield Units (HU), and the clinical variables body mass index (BMI), initial stone size, and skin-to-stone distance (SSD) were analyzed using five 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 for SWL success in the validation cohort were evaluated calculating sensitivity, specificity, and the area-under-the-curve (AUC).
RESULTS For SWL success the three clinical variables BMI, initial stone size and SSD showed AUCs of 0.68, 0.58 and 0.63 respectively, but no predictive value for HU was found. A RandomForest classifier using three 3D-TA features had an AUC of 0.79. By combining these three 3D-TA features with clinical variables, the discriminatory accuracy improved further with an AUC of 0.85 for 3D-TA features and SSD, an AUC of 0.8 for 3D-TA features and BMI and an AUC of 0.81 for 3D-TA and stone size.
CONCLUSION This preliminary study indicates that the clinical variables BMI, initial stone size and SSD show limited value for predicting SWL success, while the HU values of stones were not predictive. Selected 3D-TA features identified by machine learning provided incremental accuracy for predicting the success to SWL.

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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Diagnostic and Interventional Radiology
04 Faculty of Medicine > University Hospital Zurich > Urological Clinic
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Urology
Language:English
Date:16 April 2018
Deposited On:24 Apr 2018 11:53
Last Modified:16 Apr 2019 00:01
Publisher:Elsevier
ISSN:0022-5347
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.juro.2018.04.059
PubMed ID:29673945

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