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...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2019
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| _version_ | 1797059311322529792 |
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| author | Cui, HW Silva, MD Mills, AW North, BV Turney, BW |
| author_facet | Cui, HW Silva, MD Mills, AW North, BV Turney, BW |
| author_sort | Cui, HW |
| collection | OXFORD |
| 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. |
| first_indexed | 2024-03-06T20:02:24Z |
| format | Journal article |
| id | oxford-uuid:27c3acd8-b7b9-4752-9228-e4e95ebb8599 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:02:24Z |
| publishDate | 2019 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:27c3acd8-b7b9-4752-9228-e4e95ebb85992022-03-26T12:08:53ZPredicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variablesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:27c3acd8-b7b9-4752-9228-e4e95ebb8599EnglishSymplectic ElementsSpringer Nature2019Cui, HWSilva, MDMills, AWNorth, BVTurney, BWWe 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. |
| spellingShingle | Cui, HW Silva, MD Mills, AW North, BV Turney, BW 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 |
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