Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on CT-clinical data

FAN Rui; HE Xingyun; DU Junze; CHEN Linli

doi:10.16016/j.2097-0927.202506025

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Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on CT-clinical data

Clinical Medicine | 更新时间：2025-09-28

- Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on CT-clinical data
- Journal of Army Medical University Vol. 47, Issue 18, Pages: 2245-2253(2025)
- 作者机构：
  
  1.陆军军医大学（第三军医大学）第一附属医院：，放射科，重庆
  2.陆军军医大学（第三军医大学）第一附属医院：，乳腺甲状腺外科，重庆
  3.重庆医科大学附属第二医院放射科，重庆
- 作者简介：
- 基金信息：
  
  the Key Special Project of Technological Innovation and Application Development of Chongqing(CSTC2021jscx-gksb-N0025)
- DOI：10.16016/j.2097-0927.202506025
  CLC： R730.44;R730.7;R736.1
- Received：09 June 2025，
  
  Revised：2025-08-25，
  
  Accepted：02 September 2025，
  
  Published：30 September 2025
- 稿件说明：
移动端阅览
范锐，贺星云，杜俊泽，等.CT-临床列线图预测甲状腺微小乳头状癌中央区淋巴结转移［J］.陆军军医大学学报，2025，47（18）：2245-2253.DOI：10.16016/j.2097-0927.202506025.

FAN RUI，HE XINGYUN，DU JUNZE， et al.Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on CT-clinical data［J］.J Army Med Univ，2025，47（18）：2245-2253.DOI：10.16016/j.2097-0927.202506025.
范锐，贺星云，杜俊泽，等.CT-临床列线图预测甲状腺微小乳头状癌中央区淋巴结转移［J］.陆军军医大学学报，2025，47（18）：2245-2253.DOI：10.16016/j.2097-0927.202506025. DOI：

FAN RUI，HE XINGYUN，DU JUNZE， et al.Predictive nomogram for central lymph node metastasis in papillary thyroid microcarcinoma based on CT-clinical data［J］.J Army Med Univ，2025，47（18）：2245-2253.DOI：10.16016/j.2097-0927.202506025. DOI：

摘要

目的

探讨基于CT影像特征联合临床因素构建列线图模型对甲状腺微小乳头状癌（papillary thyroid microcarcinoma，PTMC）患者中央区淋巴结转移（central lymph node metastasis，CLNM）的预测价值。

方法

采用病例-对照研究方法，纳入2022年1月至2024年11月2家三甲医院256例经病理证实的PTMC患者，所有患者术前2周内接受颈部增强CT扫描，并行中央区淋巴结清扫术。陆军军医大学第一附属医院201例，以7∶3的比例简单随机抽样分为训练集（

=140）和内部测试集（

=61）；重庆医科大学附属第二医院的55例患者作为外部测试集。收集患者临床资料及CT影像特征，通过单因素及多因素Logistic回归分析筛选CLNM独立预测因子，构建列线图模型。采用受试者工作特征曲线（receiver operating characteristic，ROC）、曲线下面积（area under the curve，AUC）、校准曲线及决策曲线分析（decision curve analysis，DCA）评估模型区分度、校准度及临床有效性。

结果

多因素Logistic分析显示，男性（

=5.991， 95%

：2.209~18.350）、肿瘤最大直径≥0.82 cm（

=18.880， 95%

：1.803~229.500）、被膜累及（

=9.805， 95%

：4.015~26.340）、CT诊断淋巴结（

=2.872， 95%

：1.176~7.232）为CLNM独立预测因子（

0.05）。列线图模型在训练集、内部测试集和外部测试集的AUC分别为0.859（95%

：0.796~0.922）、0.786（95%

：0.671~0.901

）和0.783（95%

：0.648~0.917），校准曲线显示预测概率与实际概率高度一致（Hosmer-Lemeshow检验

0.05）。决策曲线分析表明，当阈值概率为0.18~0.80时，模型可显著提高CLNM术前识别净获益率。

结论

基于性别、肿瘤最大直径、被膜累及和CT诊断淋巴结构建的列线图模型能有效预测PTMC患者CLNM风险，为个体化手术方案制定提供量化工具，具有较高临床应用价值。

Abstract

Objective

To investigate the predictive value of a nomogram for central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) based on CT features combined with clinical factors.

Methods

A case-control study was conducted on 256 pathologically confirmed PTMC patients from 2 tertiary hospitals from January 2022 to November 2024. All participants underwent contrast-enhanced neck CT scanning within 2 weeks before surgery and received central lymph node dissection. The 201 patients from the First Affiliated Hospital of Army Medical University were randomized into a training set (

=140) and an internal validation set (

=61) in a 7∶3 ratio. The 55 patients from the Second Affiliated Hospital of Chongqing Medical University were all assigned into an external validation set. Their clinical data and CT features were collected. Univariate and multivariate logistic regression analyses were employed to identify independent predictive factors for CLNM

and then a nomogram model was constructed. Receiver operating characteristic (ROC) curve analysis (area under the curve

AUC)

calibration curve

and decision curve analysis (DCA) were performed to evaluate the model performance

discrimination and clinical utility.

Results

Multivariate logistic regression analysis identified 4 independent CLNM predictors (

0.05)

that is

male (

=5.991

95%

: 2.209~18.350)

tumor size ≥0.82 cm (

=18.880

95%

: 1.803~229.500)

capsular involvement (

=9.805

95%

: 4.015~26.340)

and CT-diagnosed lymph node positivity (

=2.872; 95%

: 1.176~7.232). The nomogram achieved an AUC value of 0.859 (95%

: 0.796~0.922)

0.786 (95%

: 0.671~0.901)

and 0.783 (95%

: 0.648~0.917) in the training and internal and external validation sets

respectively. Calibration curves demonstrated high consistency between predicted and observed probabilities (Hosmer-Lemeshow

0.05). DCA confirmed net clinical benefits for CLNM before surgical treatment with a threshold probability range of 0.18~0.80.

Conclusion

Based on sex

tumor size

capsular involvement

and CT-diagnosed lymph node metastasis

our nomogram model effectively predicts CLNM risk in PTMC patients. It can be used as a quantitative tool for personalized surgical planning and shows high clinical applicability.

关键词

Keywords

references

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