A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland
Authors Zhang D, Li X, Lv L, Yu J, Yang C, Xiong H, Liao R, Zhou B, Huang X, Liu X, Tang Z
Received 9 January 2020
Accepted for publication 2 April 2020
Published 21 April 2020 Volume 2020:12 Pages 2665—2674
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Kenan Onel
Dan Zhang,1,2 Xiaojiao Li,1,2 Liang Lv,1 Jiayi Yu,1,2 Chao Yang,1,2 Hua Xiong,1,2 Ruikun Liao,1,2 Bi Zhou,1,2 Xianlong Huang,1 Xiaoshuang Liu,3 Zhuoyue Tang1,2
1Department of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People’s Republic of China; 2Molecular and Functional Imaging Laboratory, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People’s Republic of China
Correspondence: Zhuoyue Tang
Department of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People’s Republic of China
Objective: The aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland.
Materials and Methods: Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden’s index.
Results: All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P< 0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P> 0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden’s index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%).
Conclusion: Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.
Keywords: texture analysis, epithelial tumors, parotid gland
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