| Sumario: | 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.
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