Zhang Dan, Li Xiaojiao, Lv Liang, Yu Jiayi, Yang Chao, Xiong Hua, Liao Ruikun, Zhou Bi, Huang Xianlong, Liu Xiaoshuang, Tang Zhuoyue
Department of Radiology, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People's Republic of China.
Molecular and Functional Imaging Laboratory, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 400014, People's Republic of China.
Cancer Manag Res. 2020 Apr 21;12:2665-2674. doi: 10.2147/CMAR.S245344. eCollection 2020.
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.
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.
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%).
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.
本研究旨在探讨并验证全容积CT纹理特征在鉴别腮腺常见良性和恶性上皮性肿瘤中的诊断性能。
回顾性分析83例经组织病理学证实的腮腺常见良性和恶性上皮性肿瘤患者的增强CT图像，其中多形性腺瘤（PA）患者50例，恶性上皮性肿瘤患者33例。从动脉期CT图像中提取肿瘤的定量纹理特征。通过受试者操作特征（ROC）曲线和ROC曲线下面积（AUC）评估纹理特征的诊断性能。采用最大约登指数分别讨论特异性和敏感性。
所有纹理特征均服从正态分布且具有方差齐性。上皮性恶性肿瘤组的能量、均值、相关性和总和熵均显著高于PA组（P<0.05）。PA组与上皮性恶性肿瘤组在均匀性、熵、偏度、峰度、对比度和差异熵方面无统计学差异（P>0.05）。各纹理特征及联合诊断模型的AUC分别为0.887（能量）、0.734（均值）、0.739（相关性）、0.623（总和熵）、0.888（能量-均值）、0.883（能量-相关性）、0.784（均值-相关性）。能量-均值的诊断效率最佳。基于最大约登指数，能量-相关性的特异性最高（97%），能量的敏感性最高（97%）。
能量、均值、相关性和总和熵可作为鉴别腮腺良性和恶性上皮性肿瘤的有效定量纹理特征。能量和能量-均值在鉴别腮腺良性和恶性上皮性肿瘤方面具有较高的AUC，优于其他指标或联合诊断模型。CT纹理分析可作为一种无创且有价值的手段，用于腮腺上皮性肿瘤的术前评估，且无需患者额外付费。
