Luo Zhiyan, Hong Yurong, Yan Caoxin, Ye Qin, Wang Yong, Huang Pintong
Department of Ultrasound Medicine, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Department of Pathology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Front Oncol. 2022 Oct 12;12:883429. doi: 10.3389/fonc.2022.883429. eCollection 2022.
Cervical lymph node metastasis (CLNM) is common in medullary thyroid carcinoma (MTC), but how to manage cervical lymph node involvement of clinically negative MTC is still controversial. This study evaluated the preoperative features and developed an ultrasound (US)-based nomogram to preoperatively predict the CLNM of MTC.
A total of 74 patients with histologically confirmed MTC were included in this retrospective study and assigned to the CLNM-positive group and CLNM-negative group based on the pathology. The associations between CLNM and preoperative clinical and sonographic characteristics (size, location, solid component, shape, margin, echogenicity, calcification, and extracapsular invasion of the tumor) were evaluated by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the risk of the CLNM of MTC was built and assessed in terms of discrimination, calibration, and clinical usefulness.
The nomogram was based on three factors (tumor margin, US-reported suspicious lymph node, and extracapsular invasion US features) and exhibited good discrimination with an area under the curve (AUC) of 0.919 (95% CI, 0.856-0.932). The calibration curves of the nomogram displayed a good agreement between the probability as predicted by the nomogram and the actual CLNM incidence.
We constructed and validated a US-based nomogram to predict the risk of CLNM in MTC patients, which can be easily evaluated before surgery. This model is helpful for clinical decision-making.
颈部淋巴结转移(CLNM)在甲状腺髓样癌(MTC)中很常见,但如何处理临床阴性MTC的颈部淋巴结受累仍存在争议。本研究评估了术前特征,并开发了一种基于超声(US)的列线图,以术前预测MTC的CLNM。
本回顾性研究共纳入74例经组织学确诊的MTC患者,并根据病理结果分为CLNM阳性组和CLNM阴性组。通过单变量和多变量逻辑回归分析评估CLNM与术前临床和超声特征(肿瘤大小、位置、实性成分、形状、边缘、回声、钙化和包膜外侵犯)之间的关联。构建了一个预测MTC发生CLNM风险的列线图,并从区分度、校准度和临床实用性方面进行评估。
该列线图基于三个因素(肿瘤边缘、超声报告的可疑淋巴结和超声特征的包膜外侵犯),曲线下面积(AUC)为0.919(95%CI,0.856-0.932),显示出良好的区分度。列线图的校准曲线显示列线图预测的概率与实际CLNM发生率之间具有良好的一致性。
我们构建并验证了一种基于超声的列线图,用于预测MTC患者发生CLNM的风险,该列线图在术前易于评估。该模型有助于临床决策。