Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis.
Objective: To apply texture analysis (TA) in computed tomography (CT) of urinary stones and to correlate TA findings with the number of required shockwaves for successful shock wave lithotripsy (SWL).Materials and methods: CT was performed on thirty-four urinary stones in an in vitro setting. Urinar...
| Publicado en: | Abdominal Radiology Vol. 43; no. 6; pp. 1432 - 1439 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129492046&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129492046 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jun2018 vid: 43 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129492046 10.1007/s00261-017-1309-y 129492046 ppf: 1432 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis. aug: au: Mannil, Manoj Von Spiczak, Jochen Hermanns, Thomas Alkadhi, Hatem Fankhauser, Christian D. affil: Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, 8091, Zurich, Switzerland sug: ab: Objective: To apply texture analysis (TA) in computed tomography (CT) of urinary stones and to correlate TA findings with the number of required shockwaves for successful shock wave lithotripsy (SWL).Materials and methods: CT was performed on thirty-four urinary stones in an in vitro setting. Urinary stones underwent SWL and the number of required shockwaves for disintegration was recorded. TA was performed after post-processing for pixel spacing and image normalization. Feature selection and dimension reduction were performed according to inter- and intrareader reproducibility and by evaluating the predictive ability of the number of shock waves with the degree of redundancy between TA features. Three regression models were tested: (1) linear regression with elimination of colinear attributes (2), sequential minimal optimization regression (SMOreg) employing machine learning, and (3) simple linear regression model of a single TA feature with lowest squared error.Results: Highest correlations with the absolute number of required SWL shockwaves were found for the linear regression model (<italic>r</italic> = 0.55, <italic>p</italic> = 0.005) using two weighted TA features: Histogram 10th Percentile, and Gray-Level Co-Occurrence Matrix (GLCM) S(3, 3) SumAverg. Using the median number of required shockwaves (<italic>n</italic> = 72) as a threshold, receiver-operating characteristic analysis showed largest area-under-the-curve values for the SMOreg model (AUC = 0.84, <italic>r</italic> = 0.51, <italic>p</italic> < 0.001) using four weighted TA features: Histogram 10th Percentile, and GLCM S(1, 1) InvDfMom, S(3, 3) SumAverg, and S(4, −4) SumVarnc.Conclusion: Our in vitro study illustrates the proof-of-principle of TA of urinary stone CT images for predicting the success of stone disintegration with SWL. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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