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细针穿刺报告可疑甲状腺乳头状癌或无侧颈转移的甲状腺乳头状癌时中央颈淋巴结转移的术前预测

Preoperative Prediction of Central Cervical Lymph Node Metastasis in Fine-Needle Aspiration Reporting Suspicious Papillary Thyroid Cancer or Papillary Thyroid Cancer Without Lateral Neck Metastasis.

作者信息

Zhang Kai, Qian Lang, Chen Jieying, Zhu Qian, Chang Cai

机构信息

Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.

Department of Oncology, Shanghai Medical College of Fudan University, Shanghai, China.

出版信息

Front Oncol. 2022 Mar 23;12:712723. doi: 10.3389/fonc.2022.712723. eCollection 2022.

Abstract

PURPOSE

No non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis.

PATIENTS AND METHODS

Consecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1, 2016 and June 30, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set.

RESULTS

A total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 (95%CI: 0.905-0.960, p < 0.001), 0.816, 0.966 and 0.914 in the training set and 0.967 (95%CI: 0.943-0.991, p < 0.001), 0.897, 0.959 and 0.936 in the internal validation set.

CONCLUSION

Age, mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.

摘要

目的

迄今为止,尚无无创方法能够准确判定甲状腺乳头状癌(PTC)患者是否存在中央区颈淋巴结(CCLN)转移。本研究旨在探究与CCLN转移显著相关的因素,进而构建一个模型,用于在细针穿刺活检(FNA)报告为可疑甲状腺乳头状癌(PTC)或无侧颈部转移的PTC患者中术前预测CCLN转移情况。

患者与方法

纳入2016年5月1日至2018年6月30日期间,经FNA诊断为可疑PTC或PTC且接受了甲状腺部分或全切除术及CCLN清扫术的连续住院患者。将符合条件的患者按照7:3的比例随机分为训练集和内部验证集。在训练集中进行单因素分析和多因素分析,以探究与CCLN转移相关的因素。利用与CCLN转移显著相关的因素构建预测模型,并在验证集中进行验证。

结果

本研究共有770例符合条件的患者。其中,268例患者经组织学证实存在CCLN转移,其余患者未发生转移。单因素分析和多因素分析均显示,年龄、 突变、多灶性、肿瘤大小及包膜侵犯等因素与CCLN转移显著相关。基于这些因素及超声检查CCLN状态构建的预测CCLN转移的模型,在训练集中的曲线下面积(AUC)、灵敏度、特异度及准确度分别为0.933(95%CI:0.905-0.960,p<0.001)、0.816、0.966及0.914,在内部验证集中分别为0.967(95%CI:0.943-0.991,p<0.001)、0.897、0.959及0.936。

结论

年龄、 突变、多灶性、肿瘤大小及包膜侵犯是FNA报告为可疑PTC或无侧颈部转移的PTC患者发生CCLN转移的独立预测因素。成功构建了一个简单的模型,该模型在区分有无CCLN转移的患者方面具有出色的辨别能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4d4/8983925/97f565ecf2b6/fonc-12-712723-g001.jpg

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