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一种基于套索算法的模型,用于预测术前cN0期甲状腺乳头状癌患者的中央淋巴结转移情况。

A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer.

作者信息

Zhao Feng, Wang Ping, Yu Chaoran, Song Xuefei, Wang Hui, Fang Jun, Zhu Chenfang, Li Yousheng

机构信息

Department of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Front Oncol. 2023 Jan 25;13:1034047. doi: 10.3389/fonc.2023.1034047. eCollection 2023.

Abstract

INTRODUCTION

Central lymph node metastasis (CLNM) is common in papillary thyroid carcinoma (PTC). Prophylactic central lymph node dissection (PCLND) in clinically negative central compartment lymph node (cN0) PTC patients is still controversial. How to predict CLNM before the operation is very important for surgical decision making.

METHODS

In this article, we retrospectively enrolled 243 cN0 PTC patients and gathered data including clinical characteristics, ultrasound (US) characteristics, pathological results of fine-needle aspiration (FNA), thyroid function, eight gene mutations, and immunoenzymatic results. Least absolute shrinkage and selection operator (LASSO) analysis was used for data dimensionality reduction and feature analysis.

RESULTS

According to the results, the important predictors of CLNM were identified. Multivariable logistic regression analysis was used to establish a new nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to evaluate the performance of the new prediction model.

DISCUSSION

The new nomogram prediction model was a reasonable and reliable model for predicting CLNM in cN0 PTC patients, but further validation is warranted.

摘要

引言

中央区淋巴结转移(CLNM)在甲状腺乳头状癌(PTC)中很常见。对于临床中央区淋巴结阴性(cN0)的PTC患者,预防性中央区淋巴结清扫(PCLND)仍存在争议。术前如何预测CLNM对于手术决策非常重要。

方法

在本文中,我们回顾性纳入了243例cN0 PTC患者,并收集了包括临床特征、超声(US)特征、细针穿刺活检(FNA)病理结果、甲状腺功能、8种基因突变以及免疫酶学结果等数据。采用最小绝对收缩和选择算子(LASSO)分析进行数据降维和特征分析。

结果

根据结果,确定了CLNM的重要预测因素。采用多变量逻辑回归分析建立了新的列线图预测模型。采用受试者操作特征(ROC)曲线、校准曲线和决策曲线分析(DCA)曲线评估新预测模型的性能。

讨论

新的列线图预测模型是预测cN0 PTC患者CLNM的合理且可靠的模型,但仍需进一步验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f422/9905414/9f105b3d573a/fonc-13-1034047-g001.jpg

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