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一种用于预测甲状腺微小乳头状癌患者中央区淋巴结转移风险的新工具:一项回顾性队列研究。

A novel tool for predicting the risk of central lymph node metastasis in patients with papillary thyroid microcarcinoma: a retrospective cohort study.

机构信息

The First Clinical Academy of Lanzhou University, Lanzhou, 730000, Gansu, China.

Department of Gland Surgery, HeBei General Hospital, Shijiazhuang, 050051, HeBei, China.

出版信息

BMC Cancer. 2022 Jun 2;22(1):606. doi: 10.1186/s12885-022-09655-5.

Abstract

INTRODUCTION

Central lymph node status in papillary thyroid microcarcinoma (PTMC) plays an important role in treatment decision-making clinically, however, it is not easy to predict central lymph node metastasis (CLNM). The present work focused on finding the more rational alternative for evaluating central lymph node status while identifying influencing factors to construct a model to predict CLNM incidence.

METHODS

In this study, we retrospectively analyzed the typical sonographic and clinicopathologic features of 546 PTMC patients who underwent surgery, among which, the data of 382 patients were recruited in the training cohort and that of 164 patients in the validation cohort. Based on the outcome of the training cohort, significant influencing factors were further identified through univariate analysis and were considered as independent variables in multivariable logistic regression analysis and incorporated in and presented with a nomogram.

RESULTS

In total, six independent predictors, including the age, sex, tumor size, multifocality, capsular invasion, Hashimotos thyroiditis were entered into the nomogram. Both internal validation and external validation revealed the favorable discrimination of our as-constructed nomogram. Calibration curves exhibited high consistency. As suggested by decision-curve analyses, the as-constructed nomogram might be applied in clinic. Besides, the model also distinguished patients according to risk stratification.

CONCLUSIONS

The novel nomogram containing remarkable influencing factors for CLNM cases was established in the present work. The nomogram can assist clinicians in clinical decision-making.

摘要

简介

在甲状腺乳头状微小癌(PTMC)中,中央淋巴结状态在临床治疗决策中起着重要作用,但预测中央淋巴结转移(CLNM)并不容易。本研究旨在寻找更合理的替代方法来评估中央淋巴结状态,同时确定影响因素,以构建预测 CLNM 发生率的模型。

方法

本研究回顾性分析了 546 例接受手术的 PTMC 患者的典型超声和临床病理特征,其中 382 例患者的数据纳入训练队列,164 例患者的数据纳入验证队列。基于训练队列的结果,通过单因素分析进一步确定显著的影响因素,并将其视为多变量逻辑回归分析中的自变量,并纳入和呈现为一个列线图。

结果

总共有 6 个独立的预测因素,包括年龄、性别、肿瘤大小、多灶性、包膜侵犯和桥本甲状腺炎,被纳入列线图。内部验证和外部验证均显示我们构建的列线图具有良好的区分度。校准曲线显示出高度一致性。正如决策曲线分析所建议的,所构建的列线图可在临床中应用。此外,该模型还根据风险分层区分患者。

结论

本研究建立了包含 CLNM 病例显著影响因素的新型列线图。该列线图可以帮助临床医生做出临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0c84/9164332/994468a755b3/12885_2022_9655_Fig1_HTML.jpg

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