Zhang Fan, Mei Fang, Chen Wen, Zhang Yongyue
Department of Ultrasound, Peking University Third Hospital, Beijing, China.
Department of Pathology, Peking University Third Hospital, Beijing, China.
J Ultrasound Med. 2024 Aug;43(8):1389-1399. doi: 10.1002/jum.16461. Epub 2024 Apr 5.
This study aims to identify distinct ultrasound (US) characteristics for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA), and construct a user-friendly preoperative risk stratification model for thyroid follicular neoplasms.
In this retrospective study, patients diagnosed with pathologically confirmed FTA or FTC and undergoing US examinations between July 2017 and June 2021 were designated as the training cohort, and those from July 2021 to June 2023 were enrolled as the external validation set. We systematically assessed and compared the sonographic and clinical characteristics of FTC and FTA. Univariable and multivariable logistic regression analyses were used to assess the association of US features with FTC in the training set. A prediction nomogram model, incorporating US features independently associated with FTC, was developed and validated externally to assess its performance.
A total of 645 patients (FTA/FTC = 530/115) were included in the training set, while 197 patients (FTA/FTC = 165/32) constituted the validation set. In the training set, solid composition, hypo-echogenicity, irregular margin, calcification, protrusion sign, trabecular formation, absent or thick halo, and mainly central hypervascularity were identified as independent factors associated with FTC. The prediction nomogram model constructed using these variables showed good performance in differentiating FTC from FTA with an area under the curve of 0.948 in the training set and 0.915 in the validation set.
The preoperative nomogram model constructed based on US features serves as an effective tool for the risk stratification of thyroid follicular neoplasms.
本研究旨在确定区分滤泡状甲状腺癌（FTC）与滤泡状甲状腺腺瘤（FTA）的独特超声（US）特征，并构建一个便于用户使用的甲状腺滤泡性肿瘤术前风险分层模型。
在这项回顾性研究中，将2017年7月至2021年6月期间经病理确诊为FTA或FTC并接受超声检查的患者指定为训练队列，将2021年7月至2023年6月期间的患者纳入外部验证集。我们系统地评估并比较了FTC和FTA的超声和临床特征。在训练集中，采用单变量和多变量逻辑回归分析来评估超声特征与FTC的关联。开发了一个包含与FTC独立相关的超声特征的预测列线图模型，并进行外部验证以评估其性能。
训练集共纳入645例患者（FTA/FTC = 530/115），验证集由197例患者组成（FTA/FTC = 165/32）。在训练集中，实性成分、低回声、边界不规则、钙化、突出征、小梁形成、无晕或厚晕以及主要为中央型血流丰富被确定为与FTC相关的独立因素。使用这些变量构建的预测列线图模型在区分FTC和FTA方面表现良好，训练集曲线下面积为0.948，验证集为0.915。
基于超声特征构建的术前列线图模型是甲状腺滤泡性肿瘤风险分层的有效工具。
