Zhuo Xianhua, Yu Jiandong, Chen Zhiping, Lin Zeyu, Huang Xiaoming, Chen Qin, Zhu Hongquan, Wan Yunle
Department of Hepatobiliary Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Guangzhou, China.
Department of Gastrointestinal Endoscopy, The Sixth Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Guangzhou, China.
Otolaryngol Head Neck Surg. 2022 Mar;166(3):444-453. doi: 10.1177/01945998211009858. Epub 2021 Jun 1.
To establish a dynamic nomogram based on preoperative clinical data for prediction of lateral lymph node metastasis (LLNM) of papillary thyroid carcinoma.
Retrospective study.
The Sixth Affiliated Hospital of Sun Yat-Sen University.
The data of 477 patients from 2 centers formed the training group and validation group and were retrospectively reviewed. Preoperative clinical factors influencing LLNM were identified by univariable and multivariable analysis and were to construct a predictive dynamic nomogram for LLNM. Receiver operating characteristic analysis and calibration curves were used to evaluate the predictive power of the nomogram.
The following were identified as independent risk factors for LLNM: male sex (odds ratio [OR] = 4.6, = .04), tumor size ≥10.5 mm (OR = 7.9, = .008), thyroid nodules (OR = 6.1, = .013), irregular tumor shape (OR = 24.6, = .001), rich lymph node vascularity (OR = 9.7, = .004), and lymph node location. The dynamic nomogram constructed with these factors is available at https://zxh1119.shinyapps.io/DynNomapp/. The nomogram showed good performance, with an area under the curve of 0.956 (95% CI, 0.925-0.986), a sensitivity of 0.87, and a specificity of 0.91, if high-risk patients were defined as those with a predicted probability ≥0.3 or total score ≥200. The nomogram performed well in the external validation cohort (area under the curve, 0.915; 95% CI, 0.862-0.967).
The dynamic nomogram for preoperative prediction of LLNM in papillary thyroid carcinoma can help surgeons identify high-risk patients and develop individualized treatment plans.
基于术前临床数据建立动态列线图，以预测甲状腺乳头状癌的侧方淋巴结转移（LLNM）。
回顾性研究。
中山大学附属第六医院。
来自2个中心的477例患者的数据组成训练组和验证组，并进行回顾性分析。通过单因素和多因素分析确定影响LLNM的术前临床因素，并构建LLNM的预测动态列线图。采用受试者操作特征分析和校准曲线评估列线图的预测能力。
以下因素被确定为LLNM的独立危险因素：男性（比值比[OR]=4.6，P=.04）、肿瘤大小≥10.5 mm（OR=7.9，P=.008）、甲状腺结节（OR=6.1，P=.013）、肿瘤形状不规则（OR=24.6，P=.001）、淋巴结丰富的血管（OR=9.7，P=.004）和淋巴结位置。利用这些因素构建的动态列线图可在https://zxh1119.shinyapps.io/DynNomapp/获取。如果将高危患者定义为预测概率≥0.3或总分≥200的患者，则列线图表现良好，曲线下面积为0.956（95%CI，0.925 - 0.986），灵敏度为0.87，特异度为0.91。该列线图在外部验证队列中表现良好（曲线下面积，0.915；95%CI，0.862 - 0.967）。
用于术前预测甲状腺乳头状癌LLNM的动态列线图可帮助外科医生识别高危患者并制定个体化治疗方案。
