Hao YanHong, Su Yuan, Li Yanan, Pan Qiaohong, Liu Liping
Department of Ultrasound, First Hospital of Shanxi Medical University, Taiyuan, China.
Department of Ultrasound, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
Front Oncol. 2025 May 15;15:1549148. doi: 10.3389/fonc.2025.1549148. eCollection 2025.
In oncology, the relationships among cervical central lymph node metastasis (CLNM), biochemical tests, and ultrasound characteristics in patients with papillary thyroid cancer (PTC) remain controversial. This association is currently not well supported by evidence, which emphasizes the need for further research. Understanding the connection between CLNM, biochemical testing, and ultrasound features is crucial for clinical practice and public health efforts. Research on this topic is still underway and is now receiving much interest. Our goal was to create and verify a basic cervical lymph node metastasis prediction model.
In this retrospective cohort study, 685 individuals diagnosed with PTC from First Hospital of Shanxi Medical University (n = 560) and Changzhi Heping Hospital (n = 125) participated in the research from January 2020 to October 2022. Patients were randomly assigned to a training set (n=392), an internal test set (n=168), or an external test set (n=125). Comprehensive clinical information, serological indices, and ultrasonography features were obtained for every participant. LASSO (Least Absolute Shrinkage and Selection Operator) and BSR (Best Subset Regression) to select features for model construction. A logistic regression model with filtered variables was constructed. A nomogram was developed based on six risk factors. Receiver operating characteristic (ROC) curves, decision curve analysis, and calibration curves were used to assess the predictive accuracy, clinical utility, and discriminative ability of the nomogram.
Of the 560 individuals, 54.3% (304/560) did not have lymph node metastases, whereas 45.7% (256/560) did. Age, male, nodule size, multifocal lesions, capsular contact or invasion and ill-defined margins were determined to be risk variables via BSR and multivariate logistic analysis. Nomograms were created using these six risk indicators. The prediction model of CLNM had an AUC of 0.884 (95% CI 0.851, 0.916). Both the internal and the external validation results were highly encouraging. Confirming the model's stability and applicability in different data environments.
We developed a predictive model and nomogram for CLNM in PTC patients, which demonstrated robust performance. This model can guide surgical planning, potentially reducing complications and improving outcomes.
在肿瘤学领域，甲状腺乳头状癌（PTC）患者的颈部中央淋巴结转移（CLNM）、生化检查与超声特征之间的关系仍存在争议。目前这一关联缺乏充分证据支持，因此有必要进一步开展研究。了解CLNM、生化检查与超声特征之间的联系对于临床实践和公共卫生工作至关重要。关于这一主题的研究仍在进行中，且目前受到了广泛关注。我们的目标是创建并验证一个基本的颈部淋巴结转移预测模型。
在这项回顾性队列研究中，2020年1月至2022年10月期间，来自山西医科大学第一医院（n = 560）和长治和平医院（n = 125）的685例被诊断为PTC的患者参与了研究。患者被随机分配到训练集（n = 392）、内部测试集（n = 168）或外部测试集（n = 125）。获取了每位参与者的综合临床信息、血清学指标和超声特征。采用最小绝对收缩和选择算子（LASSO）和最佳子集回归（BSR）来选择用于模型构建的特征。构建了一个包含经过筛选变量的逻辑回归模型。基于六个风险因素制定了列线图。采用受试者工作特征（ROC）曲线、决策曲线分析和校准曲线来评估列线图的预测准确性、临床实用性和判别能力。
在560例患者中，54.3%（304/560）没有淋巴结转移，而45.7%（256/560）有淋巴结转移。通过BSR和多因素逻辑分析确定年龄、男性、结节大小、多灶性病变、包膜接触或侵犯以及边界不清为风险变量。使用这六个风险指标创建了列线图。CLNM预测模型的AUC为0.884（95%CI 0.851, 0.916）。内部和外部验证结果都非常令人鼓舞，证实了该模型在不同数据环境中的稳定性和适用性。
我们为PTC患者的CLNM开发了一种预测模型和列线图，其表现出强大的性能。该模型可指导手术规划，可能减少并发症并改善治疗结果。
