Wang Cheng, Wu Xianjiang, Chen Hui, Le Qi, Dai Lei
Department of Thyroid Surgery, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China.
Gland Surg. 2022 Aug;11(8):1356-1366. doi: 10.21037/gs-22-386.
A good predictive model requires patient participation in prognostic counseling and subsequent clinical follow-up. We aimed to construct and validate a nomogram for predicting overall survival (OS) in patients with follicular thyroid cancer (FTC) after thyroidectomy.
This was a retrospective observational study. We screened 802 patients with initially diagnosed FTC from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015. Then the patients were all divided into the training set and validation set randomly at a ratio of 7:3. Univariate and multivariate Cox proportional hazard models were used to analyze the influence of different variables on OS. The concordance index (C-index) and calibration curves were used to evaluate the precision of the nomogram.
Univariate and multivariate analyses demonstrated that four factors, namely age, grade, race, and M stage (all P<0.05), were independent predictors of OS in FTC patients. Based on these factors, a predictive model was established by using the training cohort and validated by the validation cohort. A good consistency between the actual OS and predicted OS was showed by the calibration curves. Moreover, compared with the traditional tumor-node-metastasis (TNM) staging system, the nomogram had better predictive ability for the survival of patients with FTC. The C-index of the nomogram in the training set and validation set had high consistency in evaluating the survival rate of patients with FTC [training set: C-index =0.904, 95% confidence interval (CI): 0.883-0.925; validation set: C-index =0.835, 95% CI: 0.772-0.898; TNM: C-index =0.775, 95% CI: 0.732-0.818].
Based on several clinical variables, we established the first predictive model of FTC after thyroidectomy by using Cox multivariate analysis which provide a basis for each patient with prognosis and postoperative follow-up.
一个良好的预测模型需要患者参与预后咨询及后续临床随访。我们旨在构建并验证一个用于预测甲状腺滤泡癌(FTC)患者甲状腺切除术后总生存期(OS)的列线图。
这是一项回顾性观察研究。我们从监测、流行病学和最终结果(SEER)数据库中筛选出2010年至2015年间初诊为FTC的802例患者。然后将患者按照7:3的比例随机分为训练集和验证集。采用单因素和多因素Cox比例风险模型分析不同变量对OS的影响。使用一致性指数(C-index)和校准曲线评估列线图的准确性。
单因素和多因素分析表明,年龄、分级、种族和M分期这四个因素(均P<0.05)是FTC患者OS的独立预测因素。基于这些因素,利用训练队列建立了预测模型,并通过验证队列进行验证。校准曲线显示实际OS与预测OS之间具有良好的一致性。此外,与传统的肿瘤-淋巴结-转移(TNM)分期系统相比,列线图对FTC患者的生存具有更好的预测能力。列线图在训练集和验证集中的C-index在评估FTC患者生存率方面具有高度一致性[训练集:C-index =0.904,95%置信区间(CI):0.883-0.925;验证集:C-index =0.835,95% CI:0.772-0.898;TNM:C-index =0.775,95% CI:0.732-0.818]。
基于多个临床变量,我们通过Cox多因素分析建立了首个甲状腺切除术后FTC的预测模型,为每位患者的预后和术后随访提供了依据。