CT纹理分析鉴别甲状腺良恶性结节和预测淋巴结转移

目的 探讨CT纹理分析鉴别甲状腺结节良恶性和预测恶性结节淋巴结转移的价值.方法 回顾性分析174例经手术病理证实的甲状腺结节患者,包括122例良性病变(良性组)及52例恶性病变(恶性组);根据是否淋巴结转移将恶性组患者分为转移亚组(n=22)与无转移亚组(n= 30).采用Mazda软件于动脉期CT图像提取纹理特征,并...

CT texture analysis in differentiating benign and malignantthyroid nodules and predicting lymph node metastasis

Objective To investigate the value of CT texture analysis for differentiating benign and malignant thyroid nodules and predicting lymph node metastasis of malignant nodules. Methods Data of 174 patients with thyroid nodules confirmed by surgical pathology were retrospectively analyzed, including 122 benign lesions (benign group) and 52 malignant lesions (malignant group). Patients in malignant group were divided into metastatic subgroup (n=22) and non-metastatic subgroup (n=30) according to lymph nodes metastasis. Mazda software was used to extract texture features from arterial phase CT images, and Fisher method was used to reduce the dimensionality of texture features to obtain the optimal texture features between benign group and malignant group, also metastatic subgroup and non-metastatic subgroup in malignant group, respectively. The optimal texture features were compared between benign group and malignant group, also metastatic subgroup and non-metastatic subgroup. For texture features being statistical different between groups, binary Logistic regression analysis was used to analyze the independent predictors of benign and malignant thyroid nodules, and ROC curve method was used to analyze the diagnostic efficacy of independent predictors. Results A total of 279 texture features were extracted, and 10 optimal texture features were obtained between benign group and malignant group after dimension-reduction. Except for parameter S (5, -5) InvDfMom, statistical significant differences of 9 parameters were found between groups (all P<0.05). The parameter Vertl_GlevNonU and 45dgr_GlevNonU were independent predictors of benign and malignant thyroid nodules (both P<0.05). The optimal cutoff value of parameter Vertl_GlevNonU and 45dgr_GlevNonU to identify benign and malignant thyroid nodules was 21.11 and 33.61, and the AUC, sensitivity and specificity of the former was 0.76, 73.80% and 73.10%, of the latter was 0.77, 68.90% and 75.00%, respectively. No significant difference of texture features was detected between metastatic subgroup and non-metastatic subgroup (all P>0.05). Conclusion The texture features of thyroid nodules based on arterial phase CT images had certain value for distinguishing benign and malignant thyroid nodules, but only limited value in predicting lymph node metastasis of malignant thyroid nodules.