Yang Chen, Han Chun, Wang Li-ping, Feng Na, Wang Yi-fan, You Xiang-dong
Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
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Zhonghua Zhong Liu Za Zhi. 2013 Oct;35(10):758-63.
To explore the values of ultrasonographic features in differentially diagnosing benign and malignant thyroid nodules, and attempt to establish a quantitative ultrasound classification system.
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.
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 ultrasonographic features, aspect ratio ≥ 1, obscure boundary, irregular margin, significant internal hypoecho, internal hypoecho, 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 P (us) = 1 /[1+e(-)Z], where z is the logit of malignant thyroid nodule, and taking P (us) > 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. P (us) 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.
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.
探讨超声特征在甲状腺良恶性结节鉴别诊断中的价值，并尝试建立一种超声定量分类系统。
回顾性分析527例患者926个甲状腺结节的20项超声特征。采用逻辑回归分析，获得甲状腺结节恶性预测概率函数，建立初步的超声分类系统。
甲状腺结节患者年龄大于甲状腺癌患者（t = 6.496，P < 0.001）。超声诊断正确率为80.1%。逻辑多因素回归分析显示，在所有超声特征中，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、甲状腺包膜侵犯、异常颈部淋巴结及超声弹性成像5分法评分>3分是甲状腺癌的相关因素。方程为P（us）= 1 /[1+e（-）Z]，其中z为甲状腺恶性结节的logit值，以P（us）> 0.50为界值，预测准确率为84.1%。应用该模型，92.2%的甲状腺结节被预测为结节性甲状腺肿，69.4%的甲状腺癌被正确预测。P（us）分为4级：1级：恶性概率0 - 16%；2级：恶性概率17% - 50%；3级：恶性概率51% - 70%；4级：恶性概率71% - 100%。
本研究建立的甲状腺定量成像报告和数据系统使超声报告更客观、规范和标准化。可用于评估甲状腺结节的恶性风险，有助于临床正确决策。
