Jin Wen-Xu, Ye Dan-Rong, Sun Yi-Han, Zhou Xiao-Fen, Wang Ou-Chen, Zhang Xiao-Hua, Cai Ye-Feng
Department of Vascular Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325000, China.
Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325000, China,
Cancer Manag Res. 2018 Sep 4;10:3237-3243. doi: 10.2147/CMAR.S169741. eCollection 2018.
Preoperative diagnosis of central lymph node metastasis (CLNM) poses to be a challenge in clinical node-negative papillary thyroid microcarcinoma (PTMC). This research work aims at investigating the association existing between BRAF mutation, clinicopathological factors, ultrasound characteristics, and CLNM, in addition to establishing a predictive model for CLNM in PTMC.
The study included 673 PTMC patients, already undergone total thyroidectomy or lobectomy with prophylactic central lymph node dissection. The predictor factors were identified through univariate and multivariate analyses. The support vector machine was put to use to develop statistical models, which could predict CLNM on the basis of independent predictors.
Tumor size (>5 mm), lower location, no well-defined margin, contact of >25% with the adjacent capsule, display of enlarged lymph nodes, and BRAF mutation were independent predictors of CLNM. Through the use of the predictive model, 79.6% of the patients were classified accurately, the sensitivity and specificity amounted to be 85.1% and 75.8%, respectively, and the positive predictive value and negative predictive value stood at 71.6% and 87.6%, respectively.
We established a predictive model in order to predict CLNM preoperatively in PTMC when preoperative diagnosis of CLNM was not clear.
中央区淋巴结转移（CLNM）的术前诊断对临床淋巴结阴性的甲状腺微小乳头状癌（PTMC）而言是一项挑战。本研究旨在探究BRAF突变、临床病理因素、超声特征与CLNM之间的关联，此外还旨在建立PTMC中CLNM的预测模型。
该研究纳入了673例已接受全甲状腺切除术或甲状腺叶切除术并预防性中央区淋巴结清扫术的PTMC患者。通过单因素和多因素分析确定预测因素。运用支持向量机建立统计模型，该模型可基于独立预测因素预测CLNM。
肿瘤大小（>5mm）、较低位置、边界不清、与相邻包膜接触>25%、出现肿大淋巴结以及BRAF突变是CLNM的独立预测因素。通过使用该预测模型，79.6%的患者被准确分类，敏感性和特异性分别为85.1%和75.8%，阳性预测值和阴性预测值分别为71.6%和87.6%。
当CLNM的术前诊断不明确时，我们建立了一个预测模型以在PTMC中术前预测CLNM。
