超声评价甲状腺结节恶性度分级的初步探讨

Exploration on an ultrasonographic imaging reporting and data system in malignancy grading of thyroid nodules

目的 探讨超声鉴别诊断甲状腺结节良、恶性的价值,尝试建立量化分级系统.方法 回顾性分析527例甲状腺结节患者926个甲状腺结节的超声征象,并对20项指标进行评估.应用多因素Logistic回归模型,拟合出计算甲状腺结节为恶性的概率方程,根据模型进行概率预测,初步建立分级系统.结果 超声诊断良恶性甲状腺结节的准确率为80.1％.Logistic多因素回归结果显示,纵横比≥1、边界不清、边缘不规则、内部显著低回声、内部低回声、内部微钙化、后方回声衰减、甲状腺包膜受侵、周旁淋巴结异常和弹性成像5分法评分＞3分与诊断甲状腺癌结节有关.根据Logistic回归模型分析结果,建立模型为Z=Logit（P）=-3.367＋1.122X1＋1.170X2＋1.401X3＋1.170X4＋1.195X5 ＋0.758X6＋ 1.056X7＋2.082X8＋1.751X9＋0.918X10,根据Logistic回归模型计算公式Pus=1/[1＋e-z],将Z代入,以Pus＞0.50为界值,该模型预测的准确率为84.1％,其中,预测结节性甲状腺肿的准确率为92.2％,预测甲状腺癌的准确率为69.4％.根据建立的模型预测甲状腺癌,其受试者工作特征（ROC）曲线下面积为0.889（95％CI为0.865 ～0.913）.Pus建立的超声诊断甲状腺结节的4级分级系统中,1级的恶性率为0 ～16％,2级的恶性率为17％ ～ 50％,3级的恶性率为51％ ～70％,4级的恶性率为71％ ～ 100％.结论 量化分级系统可使超声报告更加客观化、规范化和标准化,可用于临床评估甲状腺结节的恶性风险度.

Objective To explore the values of ultrasonographic features in differentially diagnosing benign and malignant thyroid nodules, and attempt to establish a quantitative ultrasound classification system. Methods We retrospectively analyzed 20 ultrasound features of 926 thyroid nodules in 527 patients. Using logistic regression analysis, we obtained the probability function for predicting the malignancy in thyroid nodules and established a preliminary ultrasound classification system. Results The ages of patients with thyroid nodules was older than that of the patients with thyroid carcinoma （ t = 6. 496, P 〈0.001 ）. The correctness rate of ultrasonic diagnosis was 80. 1%. The logistic multivariate regression analysis showed that among all ultrasonographie features, aspect ratio ≥ 1, obscure boundary, irregular margin, significant internal hypoecho, internal hypoeeho, internal micro-calcifications, posterior echo attenuation, thyroid capsule invasion, abnormal adjacent lymph nodes, and ultrasonic elastography 5-point evaluation scores 〉 3 were contributing factors for thyroid carcinoma. The equation was pus = 1 / [ 1 ＋ e - z ] , where z is the logit of malignant thyroid nodule, and taking pus 〉 0.50 as boundary value, the prediction accuracy rate was 84. 1%. Using this model, 92.2% of the thyroid nodules were predicted as nodular goiter, and 69.4% of the thyroid carcinomas were correctly predicted, pu~ was stratified into four levels： Level 1 ： 0-16% malignancy; Level 2 ： 17% -50% malignancy; Level 3 ： 51% -70% malignancy; and level 4： 71%-100% malignancy. Conclusions The quantitative thyroid imaging reporting and data system developed in this study makes ultrasound reports more objective, normalized and standardized. It can be used to evaluate the malignancy risk of thyroid nodules and help to make right decision in clinics.