Purpose

We aimed to determine the ability of contrast-enhanced computed tomography (CECT) texture and histogram analyses to differentiate between benign and malignant thyroid nodules.

Materials and methods

The clinical data from 49 patients with 60 thyroid nodules were retrospectively analyzed. Nodules were classified as malignant or benign based on their histological results. Five texture and histogram parameters of thyroid nodules from CECT images, including entropy, mean, standard deviation, skewness, and kurtosis, were compared and analyzed between the two groups. Regions of interest in axial CECT images were delineated manually by two radiologists. Interobserver agreement in texture and histogram parameters between the two radiologists was assessed using the intraclass correlation coefficient (ICC). The Mann–Whitney U test and receiver operating characteristic curve analysis were conducted to estimate the diagnostic capability of texture parameters.

Results

Interobserver reproducibility (ICC = 0.919–0.969) was excellent. Among the 60 nodules, 36 were malignant and 24 were benign. Entropy of malignant thyroid nodules was significantly higher compared with benign thyroid nodules (P = 0.005). A trend toward a higher kurtosis value was observed in malignant thyroid nodules (P = 0.062). When an entropy value of 6.55 was used as a cutoff for differentiating benign from malignant thyroid nodules, the optimal area under the curve, sensitivity, and specificity were 0.716 (0.585–0.847, 95% confidence interval, P = 0.005), 75.0%, and 62.5%, respectively.

Conclusions

CECT texture and histogram analyses can be used to differentiate benign from malignant thyroid nodules.

中文翻译：

---

增强造影的计算机断层扫描纹理和直方图分析能否帮助区分恶性甲状腺结节和良性甲状腺结节？

目的

我们旨在确定对比增强计算机断层扫描（CECT）纹理和直方图分析以区分甲状腺良恶性结节的能力。

材料和方法

回顾性分析49例甲状腺结节60例的临床资料。根据其组织学结果，结节被分类为恶性或良性。比较了两组在CECT图像中甲状腺结节的五个纹理和直方图参数，包括熵，均值，标准差，偏度和峰度。两名放射科医生手动描绘了轴向CECT图像中的感兴趣区域。使用组内相关系数（ICC）评估了两位放射线医师在质地和直方图参数方面的观察者间一致性。进行了Mann-Whitney U测试和接收器工作特性曲线分析，以估计纹理参数的诊断能力。

结果

观察者之间的可重复性（ICC = 0.919–0.969）非常好。在60个结节中，恶性36个，良性24个。甲状腺良性结节的熵显着高于良性甲状腺结节（P  = 0.005）。在恶性甲状腺结节中观察到峰度值较高的趋势（P  = 0.062）。当以6.55的熵值作为区分甲状腺良恶性结节的临界值时，曲线下的最佳面积，灵敏度和特异性分别为0.716（0.585–0.847，95％置信区间，P  = 0.005），75.0％，和62.5％。

结论

CECT纹理和直方图分析可用于区分甲状腺良恶性结节与良性结节。