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Mannil M et al, 2017: Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis.

Mannil M, von Spiczak J, Hermanns T, Alkadhi H, Fankhauser CD.
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, 8091, Zurich, Switzerland.
Department of Urology, University Hospital Zurich, University of Zurich, Raemistr. 100, 8091, Zurich, Switzerland.

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.

Abdom Radiol (NY). 2017 Aug 24. doi: 10.1007/s00261-017-1309-y. [Epub ahead of print]

 

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Comments 1

Peter Alken on Friday, 12 January 2018 11:33

“Radiomics: Images Are More than Pictures, They Are Data” is the title of a recently published review (Gillies RJ, et al. Radiology. 2016 Feb;278(2):563-77) dealing with the relatively new and interesting field of analyzing the data that lead to the images of any imaging technique. Up till now these computer-based processes were predominantly applied in oncology correlating the data with patient dependent data to predict response or survival rates.
In the present paper the software was applied on stones in vitro “Overall, 308 Texture analysis (TA) features per ROI were computed. The selected TA features originate from six main categories: … A detailed description of the meaning of TA features is beyond the scope of this manuscript.” This is work in progress and more is bound to come. It may replace the sometimes conflicting data on HU and the susceptibility of stones to ESWL because the computer programs can relatively easily be applied to any CT imaging. The authors quote a publication with the nice title “Texture analysis in radiology: Does the emperor have no clothes?” (Summers RM (2017) Abdom Radiol (NY) 42(2):342–345). No wonder I did only partially understand this paper.

“Radiomics: Images Are More than Pictures, They Are Data” is the title of a recently published review (Gillies RJ, et al. Radiology. 2016 Feb;278(2):563-77) dealing with the relatively new and interesting field of analyzing the data that lead to the images of any imaging technique. Up till now these computer-based processes were predominantly applied in oncology correlating the data with patient dependent data to predict response or survival rates. In the present paper the software was applied on stones in vitro “Overall, 308 Texture analysis (TA) features per ROI were computed. The selected TA features originate from six main categories: … A detailed description of the meaning of TA features is beyond the scope of this manuscript.” This is work in progress and more is bound to come. It may replace the sometimes conflicting data on HU and the susceptibility of stones to ESWL because the computer programs can relatively easily be applied to any CT imaging. The authors quote a publication with the nice title “Texture analysis in radiology: Does the emperor have no clothes?” (Summers RM (2017) Abdom Radiol (NY) 42(2):342–345). No wonder I did only partially understand this paper.
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