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Xun Y et al., 2018: Single extracorporeal shock-wave lithotripsy for proximal ureter stones: Can CT texture analysis technique help predict the therapeutic effect?

Xun Y, Li J, Geng Y, Liu Z, Yu X, Wang X, Xiao F, Li Z, Li C, Wang S.
Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan, Hubei, 430030, China.

Abstract

PURPOSE: To explore whether the computed tomography texture analysis (CTTA) technique can help predict the curative effects of a single extracorporeal shock-wave lithotripsy (ESWL) for proximal ureteral stones. MATERIALS AND METHODS: In all, 100 patients with proximal ureteral stone underwent non-enhanced multi-detector computed tomography (MDCT) before ESWL. The patients were divided into success and failure groups. Success of ESWL was defined as the patients being stone-free or having residual stone fragments of ≤2 mm. Traditional characteristics, such as stone size, body mass index (BMI), and skin-to-stone distance (SSD), and CTTA metrics, such as the mean Hounsfield unit (HU) density, entropy, kurtosis, and skewness, were analyzed and compared between two groups by univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were generated to determine Youden index-based cutoff values. RESULT: Failure of stone removal was observed in 36 patients (36%). Stone height, stone cross-sectional diameter, largest cross-sectional area, stone volume, stone density (mean HU), and CTTA metrics (kurtosis and entropy) were the significant independent predictors of ESWL success on univariate analysis (p < 0.05). On multivariate analysis, mean HU, skewness, and kurtosis were shown to be significant predictors of ESWL success (p < 0.05). In subgroup analysis based on the cutoff value of mean stone density (HU = 857), the only significant independent factor associated with both subgroups was kurtosis (p < 0.05). CONCLUSIONS: As a quantitative analysis method, CTTA may be helpful in selecting appropriate ESWL patients. High kurtosis and low mean HU values simultaneously indicate a relatively higher ESWL success rate.

Eur J Radiol. 2018 Oct;107:84-89. doi: 10.1016/j.ejrad.2018.08.018. Epub 2018 Aug 23.

 

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Comments 1

Peter Alken on Monday, 20 May 2019 08:15

CT texture analysis has been most frequently applied to image processing of malignant tumors. There are also a few publications dealing with its application on stones:

1. Lee JY, Kim JH, Kang DH, Chung DY, Lee DH, Do Jung H, Kwon JK, Cho KS. Stone
heterogeneity index as the standard deviation of Hounsfield units: A novel predictor for shock-wave lithotripsy outcomes in ureter calculi. Sci Rep. 2016
Apr 1;6:23988. doi: 10.1038/srep23988.
2. H.W. Cui, W. Devlies, S. Ravenscroft, et al., CT texture analysis of ex vivo renal stones predicts ease of fragmentation with shockwave lithotripsy, J. Endourol. 31
(7) (2017) 694–700.
3. Mannil M, von Spiczak J, Hermanns T, Alkadhi H, Fankhauser CD. Prediction of
successful shock wave lithotripsy with CT: a phantom study using texture analysis. Abdom Radiol (NY). 2018 Jun;43(6):1432-1438. doi:
10.1007/s00261-017-1309-y.
4. Mannil M, von Spiczak J, Hermanns T, Poyet C, Alkadhi H, Fankhauser CD.
Three-Dimensional Texture Analysis with Machine Learning Provides Incremental
Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones. J Urol. 2018 Oct;200(4):829-836. doi: 10.1016/j.juro.2018.04.059.

It is a very interesting field and the authors of the present publication have gone so far as to introduce a nomogram to determine the probability of successful stone disintegration. On the other hand they state: “Finally, the texture analysis reproducibility is unstable because it is affected by many factors, and this limitation is also the focus of our next study.”

Nomograms require validation, revalidation and external confirmation. It seems to me that a reliable application of CT based texture analysis of stones to predict ESWL success is a very interesting but still long way to go.

CT texture analysis has been most frequently applied to image processing of malignant tumors. There are also a few publications dealing with its application on stones: 1. Lee JY, Kim JH, Kang DH, Chung DY, Lee DH, Do Jung H, Kwon JK, Cho KS. Stone heterogeneity index as the standard deviation of Hounsfield units: A novel predictor for shock-wave lithotripsy outcomes in ureter calculi. Sci Rep. 2016 Apr 1;6:23988. doi: 10.1038/srep23988. 2. H.W. Cui, W. Devlies, S. Ravenscroft, et al., CT texture analysis of ex vivo renal stones predicts ease of fragmentation with shockwave lithotripsy, J. Endourol. 31 (7) (2017) 694–700. 3. Mannil M, von Spiczak J, Hermanns T, Alkadhi H, Fankhauser CD. Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis. Abdom Radiol (NY). 2018 Jun;43(6):1432-1438. doi: 10.1007/s00261-017-1309-y. 4. Mannil M, von Spiczak J, Hermanns T, Poyet C, Alkadhi H, Fankhauser CD. Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones. J Urol. 2018 Oct;200(4):829-836. doi: 10.1016/j.juro.2018.04.059. It is a very interesting field and the authors of the present publication have gone so far as to introduce a nomogram to determine the probability of successful stone disintegration. On the other hand they state: “Finally, the texture analysis reproducibility is unstable because it is affected by many factors, and this limitation is also the focus of our next study.” Nomograms require validation, revalidation and external confirmation. It seems to me that a reliable application of CT based texture analysis of stones to predict ESWL success is a very interesting but still long way to go.
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