PLT、NLR、PLR、甲状腺功能相关指标与DTC颈部淋巴结转移的关系及预测模型构建

王春燕; 周兰柱

doi:10.16766/j.cnki.issn.1674-4152.004409

PLT、NLR、PLR、甲状腺功能相关指标与DTC颈部淋巴结转移的关系及预测模型构建

doi: 10.16766/j.cnki.issn.1674-4152.004409

王春燕¹,
周兰柱^2, ,

1.
蚌埠医科大学第二附属医院耳鼻咽喉头颈外科，安徽蚌埠 233000
2.
蚌埠医科大学第一附属医院耳鼻咽喉头颈外科，安徽蚌埠 233004

基金项目:

安徽省高等学校科学研究项目 2023AH051977

详细信息

通讯作者:
周兰柱, E-mail：lanzhu-07@163.com

中图分类号: R736.1
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- 文章访问数: 13
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- 被引次数: 0
出版历程
- 收稿日期: 2025-11-03
- 网络出版日期: 2026-06-02

A predictive model for cervical lymph node metastasis in differentiated thyroid carcinoma based on PLT, NLR, PLR, and thyroid function parameters

WANG Chunyan¹,
ZHOU Lanzhu^{2
, ,}

1.
Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233000, China

摘要

摘要: 目的分析血小板计数(PLT)、中性粒细胞/淋巴细胞比值(NLR)、血小板/淋巴细胞比值(PLR)、甲状腺功能相关指标与分化型甲状腺癌(DTC)合并颈部淋巴结转移的关系，为优化DTC合并颈部淋巴结转移患者的诊疗策略提供参考。方法选取2020年1月—2024年1月蚌埠医科大学第一附属医院收治的221例DTC患者，依据是否合并颈部淋巴结转移分为发生组(78例)和未发生组(143例)。比较2组PLR、NLR、PLR、甲状腺功能相关指标差异。采用多因素logistic回归分析研究DTC合并颈部淋巴结转移的影响因素。绘制ROC曲线，评估各独立危险因素单独及联合应用对DTC合并颈部淋巴结转移的预测效能。结果发生组临床分期Ⅱ期占比、PLT、NLR、PLR、促甲状腺激素(TSH)、甲状腺球蛋白(Tg)均高于未发生组(P < 0.05)。二元logistic回归分析显示，临床分期、PLT、NLR、PLR、TSH、Tg均是DTC合并颈部淋巴结转移的独立影响因素(P < 0.05)。建立DTC合并颈部淋巴结转移的列线图风险预测模型，ROC分析显示，临床分期、PLT、NLR、PLR、TSH、Tg、列线图风险预测模型的曲线下面积分别为0.573、0.720、0.613、0.738、0.751、0.718、0.898。结论临床分期Ⅱ期及PLT、NLR、PLR、TSH、Tg升高为DTC合并颈部淋巴结转移的独立危险因素，基于此构建的列线图预测模型具有高预测效能与临床实用性。
- 分化型甲状腺癌 /
- 血小板计数 /
- 中性粒细胞/淋巴细胞比值 /
- 血小板/淋巴细胞比值 /
- 甲状腺功能 /
- 淋巴结转移 /
- 列线图
Abstract: Objective To analyze the relationship between platelet count (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), thyroid function related indicators, and cervical lymph node metastasis in differentiated thyroid cancer (DTC), and to provide a reference for optimizing the diagnosis and treatment strategy of DTC patients. Methods A total of 221 DTC patients admitted to the First Affiliated Hospital of Bengbu Medical University between January 2020 and January 2024 were enrolled. According to the presence or absence of cervical lymph node metastasis (LNM), the patients were divided into a metastasis group (n = 78) and a non-metastasis group (n = 143). Differences in PLT, NLR, PLR, and thyroid function-related indicators were compared between the two groups. Multivariate logistic regression analysis was used to analyze the influencing factors for DTC with cervical lymph node metastasis. ROC curves were plotted to evaluate the predictive performance of each independent risk factor individually and in combination for DTC with cervical lymph node metastasis. Results The proportion of patients in clinical stage Ⅱ, as well as the levels of PLT, NLR, PLR, thyroid-stimulating hormone (TSH), and thyroglobulin (Tg), were higher in the metastasis group than in the non-metastasis group (P < 0.05). Binary logistic regression analysis showed that clinical stage, PLT, NLR, PLR, TSH, and Tg were independent risk factors for DTC with cervical lymph node metastasis (P < 0.05). A nomogram risk prediction model for DTC with cervical lymph node metastasis was established. ROC analysis showed that the areas under the curve (AUC) for clinical stage, PLT, NLR, PLR, TSH, Tg, and the nomogram prediction model were 0.573, 0.720, 0.613, 0.738, 0.751, 0.718, and 0.898, respectively. Conclusion Clinical stage Ⅱ and elevated PLT, NLR, PLR, TSH, and Tg are independent risk factors for DTC with cervical lymph node metastasis. The nomogram prediction model constructed based on these factors demonstrates high predictive performance and clinical utility.
- Differentiated thyroid carcinoma /
- Platelet count /
- Neutrophil-to-lymphocyte ratio /
- Platelet-to-lymphocyte ratio /
- Thyroid function /
- Lymph node metastasis /
- Nomogram

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图 1 DTC合并颈部淋巴结转移的列线图风险预测模型构建

Figure 1. Construction of a nomogram risk prediction model for DTC with cervical lymph node metastasis

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图 2 列线图风险预测模型的ROC曲线

Figure 2. ROC curve of nomogram risk prediction model

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图 3 列线图风险预测模型的校准曲线

Figure 3. Calibration curve of the nomogram risk prediction model

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图 4 列线图风险预测模型的决策曲线

Figure 4. Decision curve of the nomogram risk prediction model

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表 1 发生与未发生颈部淋巴结转移的DTC患者临床资料比较

Table 1. Comparison of clinical data of DTC patients with and without cervical lymph node metastasis

指标	发生组 (n=78)	未发生组 (n=143)	统计量	P值
性别(例)			0.108^a	0.743
男性	49	93
女性	29	50
年龄(x±s，岁)	46.32±9.17	44.17±10.69	1.571^b	0.118
临床分期(例)			9.278^a	0.002
Ⅱ期	18	12
Ⅰ期	60	131
WBC(x±s，×10⁹/L)	6.15±1.30	5.98±1.53	0.843^b	0.400
NE(x±s，×10⁹/L)	3.63±0.95	3.48±1.11	1.008^b	0.315
LY(x±s，×10⁹/L)	2.01±0.61	1.99±0.59	0.236^b	0.814
PLT(x±s，×10⁹/L)	286.78±62.07	238.78±55.58	5.885^b	＜0.001
MPV(x±s，fL)	10.53±1.05	10.49±1.31	0.247^b	0.805
PCT(x±s，%)	0.26±0.06	0.25±0.04	1.190^b	0.236
PDW(x±s，%)	14.38±2.23	14.26±3.05	0.335^b	0.738
NLR(x±s)	2.52±0.86	2.14±0.75	3.411^b	0.001
PLR(x±s)	180.33±26.38	158.70±19.85	6.326^b	＜0.001
TSH(x±s，mIU/L)	3.18±0.56	2.65±0.51	7.197^b	＜0.001
Tg(x±s，μg/L)	34.51±5.39	29.73±6.09	5.818^b	＜0.001
TPOAb(x±s，IU/mL)	5.80±0.97	5.67±1.04	0.914^b	0.362
注：^a为χ²值，^b为t值。

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表 2 DTC合并颈部淋巴结转移影响因素的多因素logistic回归分析

Table 2. Multivariate logistic regression analysis of risk factors for DTC with cervical lymph node metastasis

变量	B	SE	Waldχ²	P值	OR(95% CI)
临床分期	1.854	0.641	8.365	0.004	6.386(1.818~22.433)
PLT	0.012	0.003	12.225	＜0.001	1.012(1.005~1.019)
NLR	0.754	0.271	7.745	0.005	2.126(1.250~3.615)
PLR	0.038	0.010	15.501	＜0.001	1.039(1.019~1.059)
TSH	1.871	0.416	20.254	＜0.001	6.494(2.875~14.667)
Tg	0.128	0.036	12.691	＜0.001	1.136(1.059~1.219)

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表 3 ROC曲线对DTC合并颈部淋巴结转移的独立危险因素及预测模型的分析

Table 3. Analysis of independent risk factors and predictive model for DTC with cervical lymph node metastasis using ROC curve

检验变量	cut-off值	AUC	95% CI	SE	P值	约登指数	灵敏度	特异度
临床分期	0.500	0.573	0.492~0.655	0.041	0.071	0.147	0.916	0.231
PLT(×10⁹/L)	262.085	0.720	0.649~0.790	0.036	< 0.001	0.373	0.667	0.706
NLR	2.565	0.613	0.534~0.692	0.040	< 0.001	0.193	0.487	0.706
PLR	166.600	0.738	0.666~0.809	0.037	< 0.001	0.437	0.731	0.706
TSH(mIU/L)	3.080	0.751	0.683~0.819	0.035	< 0.001	0.388	0.577	0.811
Tg(μg/L)	34.755	0.718	0.649~0.786	0.035	< 0.001	0.328	0.538	0.790
列线图风险预测模型	0.317	0.898	0.855~0.940	0.022	< 0.001	0.651	0.833	0.818

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