Department of Thyroid Surgery, The First Hospital of China Medical University, Shenyang, China.
Front Endocrinol (Lausanne). 2023 Jan 19;14:1112506. doi: 10.3389/fendo.2023.1112506. eCollection 2023.
Whether routine central lymph node dissection (CLND) is necessary for T1-T2 papillary thyroid carcinoma (PTC) patients without certain lateral lymph node metastases (LLNM) remains controversial. This study aims to construct a nomogram that predicts central lymph node metastasis (CLNM) for T1-T2 PTC patients without LLNM.
We retrospectively reviewed adult T1-T2 PTC patients with no LLNM retrieved from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We also collected data from patients treated at the First Hospital of China Medical University between February and April 2021 for external validation. Logistic regression model was used to construct a risk prediction model nomogram. The receiver-operating characteristic (ROC) curve, calibration plot, and decision curve analyses (DCA) were used for assessing the nomogram.
5,094 patients from the SEER database and 300 patients from our department were finally included in this study. Variables such as age, gender, race, tumor size, multifocality, and minimal extrathyroidal extension (mETE) were found to be associated with CLNM and were subsequently incorporated into our nomogram. The C-index of our constructed model was 0.704, while the internal and external validation C-indexes were 0.693 and 0.745, respectively. The nomogram was then evaluated using calibration and decision curve analyses.
A visualized nomogram was successfully developed to predict CLNM in T1-T2 PTC patients without LLNM and assist clinicians in making personalized clinical decisions.
对于无特定侧方淋巴结转移(LLNM)的 T1-T2 甲状腺乳头状癌(PTC)患者,是否需要常规行中央区淋巴结清扫术(CLND)仍存在争议。本研究旨在构建一个预测 T1-T2 无 LLNM PTC 患者中央区淋巴结转移(CLNM)的列线图。
我们回顾性分析了 2010 年至 2015 年期间来自监测、流行病学和最终结果(SEER)数据库的无 LLNM 的成年 T1-T2 PTC 患者数据,并收集了 2021 年 2 月至 4 月期间在中国医科大学第一附属医院治疗的患者数据进行外部验证。使用逻辑回归模型构建风险预测模型列线图。使用受试者工作特征(ROC)曲线、校准图和决策曲线分析(DCA)评估该列线图。
最终纳入了来自 SEER 数据库的 5094 例患者和我们科室的 300 例患者。年龄、性别、种族、肿瘤大小、多灶性和最小甲状腺外侵犯(mETE)等变量与 CLNM 相关,并被纳入我们的列线图中。我们构建的模型的 C 指数为 0.704,内部和外部验证的 C 指数分别为 0.693 和 0.745。随后,我们使用校准和决策曲线分析评估了该列线图。
成功开发了一个可视化列线图,用于预测无 LLNM 的 T1-T2 PTC 患者的 CLNM,以帮助临床医生做出个性化的临床决策。