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基于列线图的孤立峡部型甲状腺微小乳头状癌中央区淋巴结转移风险预测:一项 2010 年至 2021 年的回顾性研究。

Risk prediction for central lymph node metastasis in isolated isthmic papillary thyroid carcinoma by nomogram: A retrospective study from 2010 to 2021.

机构信息

Department of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Front Endocrinol (Lausanne). 2023 Jan 17;13:1098204. doi: 10.3389/fendo.2022.1098204. eCollection 2022.

Abstract

BACKGROUND

Isthmic papillary thyroid carcinoma (IPTC) is an aggressive thyroid cancer associated with a poor prognosis. Guidelines elaborating on the extent of surgery for IPTC are yet to be developed. This study aims to construct and validate a model to predict central lymph node metastasis (CLNM) in patients with IPTC, which could be used as a risk stratification tool to determine the best surgical approach for patients.

METHODS

Electronic medical records for patients diagnosed with isolated papillary thyroid carcinoma who underwent surgery at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, from January 2010 to December 2021 were reviewed. All patients who underwent thyroidectomy with central neck dissection (CND) for isolated IPTC were included. We conducted univariate and multivariate logistic regression analyses to assess risk factors for ipsilateral and contralateral CLNM and the number of CLNM in IPTC patients. Based on the analysis, the nomogram construction and internal validations were performed.

RESULTS

A total of 147 patients with isolated IPTC were included. The occurrence of CLNM was 53.7% in the patients. We identified three predictors of ipsilateral CLNM, including age, gender, and size. For contralateral CLNM, three identified predictors were age, gender, and capsular invasion. Predictors for the number of CLNM included age, gender, capsular invasion, tumor size, and chronic lymphocytic thyroiditis (CLT). The concordance index(C-index) of the models predicting ipsilateral CLNM, contralateral CLNM, 1-4 CLNM, and ≥5 CLNM was 0.779 (95%CI, 0.704, to 0.854), 0.779 (95%CI, 0.703 to 0.855), 0.724 (95%CI, 0.629 to 0.818), and 0.932 (95%CI, 0.884 to 0.980), respectively. The corresponding indices for the internal validation were 0.756 (95%CI, 0.753 to 0.758), 0.753 (95%CI, 0.750 to 0.756), 0.706 (95%CI, 0.702 to 0.708), and 0.920 (95%CI, 0.918 to 0.922). Receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA) results confirmed that the three nomograms could precisely predict CLNM in patients with isolated IPTC.

CONCLUSION

We constructed predictive nomograms for CLNM in IPTC patients. A risk stratification scheme and corresponding surgical treatment recommendations were provided accordingly. Our predictive models can be used as a risk stratification tool to help clinicians make individualized surgical plans for their patients.

摘要

背景

峡部甲状腺乳头状癌(IPTC)是一种侵袭性甲状腺癌,预后较差。目前尚未制定出专门针对 IPTC 手术范围的指南。本研究旨在构建并验证一种预测 IPTC 患者中央淋巴结转移(CLNM)的模型,该模型可用作风险分层工具,以确定患者的最佳手术方式。

方法

回顾性分析 2010 年 1 月至 2021 年 12 月期间在华中科技大学同济医学院附属协和医院接受手术治疗的孤立性甲状腺乳头状癌患者的电子病历。所有接受甲状腺全切术联合中央颈部淋巴结清扫术(CND)治疗孤立性 IPTC 的患者均被纳入研究。我们进行了单因素和多因素逻辑回归分析,以评估 IPTC 患者发生同侧和对侧 CLNM 以及 CLNM 数量的相关风险因素。在此基础上,我们进行了列线图的构建和内部验证。

结果

共纳入 147 例孤立性 IPTC 患者。CLNM 的发生率为 53.7%。我们确定了三个预测同侧 CLNM 的因素,包括年龄、性别和肿瘤大小。对于对侧 CLNM,我们发现三个预测因素为年龄、性别和包膜侵犯。预测 CLNM 数量的因素包括年龄、性别、包膜侵犯、肿瘤大小和慢性淋巴细胞性甲状腺炎(CLT)。预测同侧 CLNM、对侧 CLNM、1-4 个 CLNM 和≥5 个 CLNM 的模型的一致性指数(C-index)分别为 0.779(95%CI,0.704 至 0.854)、0.779(95%CI,0.703 至 0.855)、0.724(95%CI,0.629 至 0.818)和 0.932(95%CI,0.884 至 0.980)。内部验证的相应指数分别为 0.756(95%CI,0.753 至 0.758)、0.753(95%CI,0.750 至 0.756)、0.706(95%CI,0.702 至 0.708)和 0.920(95%CI,0.918 至 0.922)。受试者工作特征(ROC)曲线、校准和决策曲线分析(DCA)结果证实,这三个列线图能够准确预测孤立性 IPTC 患者的 CLNM。

结论

我们构建了预测 IPTC 患者 CLNM 的列线图,并提供了相应的风险分层方案和手术治疗建议。我们的预测模型可用作风险分层工具,帮助临床医生为患者制定个体化的手术方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea83/9886574/c1ce30d5890f/fendo-13-1098204-g001.jpg

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