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Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis

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Abstract

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 (r = 0.55, p = 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 (n = 72) as a threshold, receiver-operating characteristic analysis showed largest area-under-the-curve values for the SMOreg model (AUC = 0.84, r = 0.51, p < 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.

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Authors and Affiliations

  1. Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, 8091, Zurich, Switzerland

    Manoj Mannil, Jochen von Spiczak & Hatem Alkadhi

  2. Department of Urology, University Hospital Zurich, University of Zurich, Raemistr. 100, 8091, Zurich, Switzerland

    Thomas Hermanns & Christian D. Fankhauser

Authors
  1. Manoj Mannil
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  2. Jochen von Spiczak
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  3. Thomas Hermanns
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  4. Hatem Alkadhi
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  5. Christian D. Fankhauser
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Corresponding author

Correspondence to Hatem Alkadhi.

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Funding

No external funding was received for this study.

Conflict of interest

None of the authors has any conflict of interest to declare.

Ethical approval

Local ethics committee approval was not required for this phantom study.

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Mannil, M., von Spiczak, J., Hermanns, T. et al. Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis. Abdom Radiol 43, 1432–1438 (2018). https://doi.org/10.1007/s00261-017-1309-y

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  • DOI: https://doi.org/10.1007/s00261-017-1309-y

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