Research Article
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Published Online: 1 July 2017

CT Texture Analysis of Ex Vivo Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy

Publication: Journal of Endourology
Volume 31, Issue Number 7

Abstract

Introduction: Understanding the factors affecting success of extracorporeal shockwave lithotripsy (SWL) would improve informed decision-making on the most appropriate treatment modality for an individual patient. Although stone size and skin-to-stone distance do correlate with fragmentation efficacy, it has been shown that stone composition and architecture, as reflected by structural heterogeneity on CT, are also important factors. This study aims to determine if CT texture analysis (CTTA), a novel, nondestructive, and objective tool that generates statistical metrics reflecting stone heterogeneity, could have utility in predicting likelihood of SWL success.
Materials and Methods: Seven spontaneously passed, intact renal tract stones, were scanned ex vivo using standard CT KUB and micro-CT. The stones were then fragmented in vitro using a clinical lithotripter, after which, chemical composition analysis was performed. CTTA was used to generate a number of metrics that were correlated to the number of shocks needed to fragment the stone.
Results: CTTA metrics reflected stone characteristics and composition, and predicted ease of SWL fragmentation. The strongest correlation with number of shocks required to fragment the stone was mean Hounsfield unit (HU) density (r = 0.806, p = 0.028) and a CTTA metric measuring the entropy of the pixel distribution of the stone image (r = 0.804, p = 0.039). Using multiple linear regression analysis, the best model showed that CTTA metrics of entropy and kurtosis could predict 92% of the outcome of number of shocks needed to fragment the stone. This was superior to using stone volume or density.
Conclusions: CTTA metrics entropy and kurtosis have been shown in this experimental ex vivo setting to strongly predict fragmentation by SWL. This warrants further investigation in a larger clinical study for the contribution of CT textural metrics as a measure of stone heterogeneity, along with other known clinical factors, to predict likelihood of SWL success.

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Information & Authors

Information

Published In

cover image Journal of Endourology
Journal of Endourology
Volume 31Issue Number 7July 2017
Pages: 694 - 700
PubMed: 28474533

History

Published in print: July 2017
Published online: 1 July 2017
Published ahead of print: 5 June 2017
Published ahead of production: 5 May 2017

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Authors

Affiliations

Helen W. Cui*
Oxford Stone Group, University of Oxford, Oxford, United Kingdom.
Wout Devlies*
Faculty of Medicine, KU Leuven, Leuven, Belgium.
Samuel Ravenscroft
Division of Medical Sciences, University of Oxford, Oxford, United Kingdom.
Hendrik Heers
Oxford Stone Group, University of Oxford, Oxford, United Kingdom.
Department of Urology and Paediatric Urology, Philipps-Universität Marburg, Marburg, Germany.
Andrew J. Freidin
Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom.
Robin O. Cleveland
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
Balaji Ganeshan
Division of Medicine, Institute of Nuclear Medicine, University College London, London, United Kingdom.
Benjamin W. Turney
Oxford Stone Group, University of Oxford, Oxford, United Kingdom.

Notes

*
These authors contributed equally to this work.
Address correspondence to:Helen Wei CuiOxford Stone GroupUniversity of OxfordUrology DepartmentChurchill HospitalOld RoadOxfordOX3 7LEUnited Kingdom
E-mail: [email protected]

Author Disclosure Statement

One author (B.G.) is a director, part-time employee, and shareholder of Feedback Plc (Cambridge, England, United Kingdom), company that develops and markets the TexRAD texture analysis algorithm described in this article. All other authors declare that no competing financial interests exist.

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