Chen Wanzhi, Yu Jichun, Lei Kunlin, Xie Rong, Wang Haiyan, Zhong Meijun
Department of Thyroid Surgery, The Second Affiliated Hospital of Nanchang University Nanchang 330006, Jiangxi, P. R. China.
Am J Cancer Res. 2024 Jun 15;14(6):3104-3116. doi: 10.62347/HDNA2969. eCollection 2024.
This study aimed to identify risk factors for cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) and develop a clinical prediction model. Retrospectively, data were collected from 348 PTC patients treated at the Second Affiliated Hospital of Nanchang University between January 2019 and December 2022, with 241 patients included in the final analyses. Patients with lateral cervical LNM were categorized into a metastasis group, and those without were in a non-metastasis group. The patients were divided into a training set (n=169) and a validation set (n=72) in a 7:3 ratio. Logistic and least absolute shrinkage and selection operator (LASSO) regression models were used to identify key factors associated with lateral cervical LNM and prognosis, enabling the construction of a predictive model. The model's validity was assessed via the Hosmer-Lemeshow Test, calibration curves, ROC curves, and decision curve analysis. The metastasis group exhibited higher proportions of males, multiple lesions, bilateral involvement, tumor diameter ≥1 cm, and elevated levels of PLR, LMR, and NLR (P<0.05). Logistic regression analysis revealed that gender, multiple lesions, affected side, and tumor diameter were associated with lateral cervical LNM (P<0.05). The predictive Nomogram model, which included factors like affected side, tumor diameter, capsular invasion, central LNM, PLR, and NLR, demonstrated strong predictive accuracy and clinical utility. Thus, this study provides a practical clinical tool through an accurate Nomogram model to assess lateral cervical LNM risk in PTC patients using logistic and LASSO regression analyses.
本研究旨在确定乳头状甲状腺癌（PTC）颈部淋巴结转移（LNM）的危险因素，并建立一个临床预测模型。回顾性收集了2019年1月至2022年12月在南昌大学第二附属医院接受治疗的348例PTC患者的数据，最终纳入分析241例患者。将有侧颈部LNM的患者分为转移组，无侧颈部LNM的患者分为非转移组。患者按7:3的比例分为训练集（n = 169）和验证集（n = 72）。采用逻辑回归和最小绝对收缩和选择算子（LASSO）回归模型来确定与侧颈部LNM和预后相关的关键因素，从而构建预测模型。通过Hosmer-Lemeshow检验、校准曲线、ROC曲线和决策曲线分析评估模型的有效性。转移组男性、多灶性病变、双侧受累、肿瘤直径≥1 cm以及PLR、LMR和NLR水平升高的比例更高（P<0.05）。逻辑回归分析显示，性别、多灶性病变、患侧和肿瘤直径与侧颈部LNM相关（P<0.05）。包括患侧、肿瘤直径、包膜侵犯、中央区LNM、PLR和NLR等因素的预测列线图模型显示出较强的预测准确性和临床实用性。因此，本研究通过精确的列线图模型，利用逻辑回归和LASSO回归分析，为评估PTC患者侧颈部LNM风险提供了一种实用的临床工具。
