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Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables

We aimed to develop and evaluate a statistical model, which included known pre-treatment factors and new computed tomography texture analysis (CTTA) variables, for its ability to predict the likelihood of a successful outcome after extracorporeal shockwave lithotripsy (SWL) treatment for renal and u...

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Autores principales: Cui, Helen W., Silva, Mafalda D., Mills, Andrew W., North, Bernard V., Turney, Benjamin W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788981/
https://www.ncbi.nlm.nih.gov/pubmed/31604986
http://dx.doi.org/10.1038/s41598-019-51026-x
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author Cui, Helen W.
Silva, Mafalda D.
Mills, Andrew W.
North, Bernard V.
Turney, Benjamin W.
author_facet Cui, Helen W.
Silva, Mafalda D.
Mills, Andrew W.
North, Bernard V.
Turney, Benjamin W.
author_sort Cui, Helen W.
collection PubMed
description We aimed to develop and evaluate a statistical model, which included known pre-treatment factors and new computed tomography texture analysis (CTTA) variables, for its ability to predict the likelihood of a successful outcome after extracorporeal shockwave lithotripsy (SWL) treatment for renal and ureteric stones. Up to half of patients undergoing SWL may fail treatment. Better prediction of which cases will likely succeed SWL will help patients to make an informed decision on the most effective treatment modality for their stone. 19 pre-treatment factors for SWL success, including 6 CTTA variables, were collected from 459 SWL cases at a single centre. Univariate and multivariable analyses were performed by independent statisticians to predict the probability of a stone free (both with and without residual fragments) outcome after SWL. A multivariable model had an overall accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome. The variables most frequently chosen for the model were those which represented stone size. Although previous studies have suggested SWL can be reliably predicted using pre-treatment factors and that analysis of CT stone images may improve outcome prediction, the results from this study have not produced a useful model for SWL outcome prediction.
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spelling pubmed-67889812019-10-17 Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables Cui, Helen W. Silva, Mafalda D. Mills, Andrew W. North, Bernard V. Turney, Benjamin W. Sci Rep Article We aimed to develop and evaluate a statistical model, which included known pre-treatment factors and new computed tomography texture analysis (CTTA) variables, for its ability to predict the likelihood of a successful outcome after extracorporeal shockwave lithotripsy (SWL) treatment for renal and ureteric stones. Up to half of patients undergoing SWL may fail treatment. Better prediction of which cases will likely succeed SWL will help patients to make an informed decision on the most effective treatment modality for their stone. 19 pre-treatment factors for SWL success, including 6 CTTA variables, were collected from 459 SWL cases at a single centre. Univariate and multivariable analyses were performed by independent statisticians to predict the probability of a stone free (both with and without residual fragments) outcome after SWL. A multivariable model had an overall accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome. The variables most frequently chosen for the model were those which represented stone size. Although previous studies have suggested SWL can be reliably predicted using pre-treatment factors and that analysis of CT stone images may improve outcome prediction, the results from this study have not produced a useful model for SWL outcome prediction. Nature Publishing Group UK 2019-10-11 /pmc/articles/PMC6788981/ /pubmed/31604986 http://dx.doi.org/10.1038/s41598-019-51026-x Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Cui, Helen W.
Silva, Mafalda D.
Mills, Andrew W.
North, Bernard V.
Turney, Benjamin W.
Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
title Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
title_full Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
title_fullStr Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
title_full_unstemmed Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
title_short Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
title_sort predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788981/
https://www.ncbi.nlm.nih.gov/pubmed/31604986
http://dx.doi.org/10.1038/s41598-019-51026-x
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