Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma (original) (raw)

Abstract

Purpose

To investigate the feasibility of using CT texture analysis (CTTA) to differentiate between low- versus high-grade urothelial carcinoma.

Methods

A total of 105 patients with high-grade urothelial carcinoma (HGUC, n = 106) and low-grade urothelial carcinoma (LGUC, n = 18) were included in this retrospective study. Both unenhanced and enhanced CT images representing the largest cross-sectional area of the tumor were chosen for CTTA performed using TexRAD software. Comparison of texture parameters, mean gray-level intensity (Mean), standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were made for the objective. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve was calculated for texture parameters that were significantly different (P < 0.05) for the purpose. Sensitivity (Se), specificity (Sp), positive predictive value, negative predictive value, and accuracy were calculated using the cut-off value of texture parameter with the highest AUC.

Results

Compared to HGUC, LGUC had significantly lower Mean (P = 0.001), Entropy (P = 0.002), and MPP (P < 0.001) on unenhanced and enhanced images and lower SD (P = 0.048) on enhanced images. There was no significant difference in skewness or kurtosis at any texture scale on unenhanced and enhanced images. A MPP <24.13 at fine texture scale on unenhanced images identified LGUC from HGUC with the highest AUC of 0.779 ± 0.065 (Se = 72.2%, Sp = 84.9%, PPV = 44.8%, NPV = 94.7%, and accuracy = 83.1%).

Conclusions

CTTA proved to be a feasible tool for differentiating LGUC from HGUC. MPP quantified from fine texture scale on unenhanced images was the optimal diagnostic parameter for estimating histologic grade of urothelial carcinoma.

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Acknowledgments

We had like to thank Dr. Balaji Ganeshan from Feedback Plc, Cambridge, England, UK, for his guidance and assistance to using the TexRAD software. This study was funded by the Health Industry Special Scientific Research Project of China (Grant Number 201402019).

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Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
    Gu-Mu-Yang Zhang, Hao Sun, Bing Shi, Zheng-Yu Jin & Hua-Dan Xue

Authors

  1. Gu-Mu-Yang Zhang
  2. Hao Sun
  3. Bing Shi
  4. Zheng-Yu Jin
  5. Hua-Dan Xue

Corresponding authors

Correspondence toHao Sun, Zheng-Yu Jin or Hua-Dan Xue.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent from all individual participants included in this retrospective study was waived by the institutional review board.

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261_2016_897_MOESM1_ESM.pdf (download PDF )

Texture parameters for discriminating LGUC from HGUC on unenhanced and enhanced CT images: (a) IQR interquartile ranges., (b) Data are median of each parameter per lesion and data in parentheses are inter-quartile ranges, unless otherwise indicated (PDF 18 kb)

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Zhang, GMY., Sun, H., Shi, B. et al. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma.Abdom Radiol 42, 561–568 (2017). https://doi.org/10.1007/s00261-016-0897-2

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