Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China.
Thorac Cancer. 2020 Sep;11(9):2457-2464. doi: 10.1111/1759-7714.13556. Epub 2020 Jul 12.
The purpose of this study was to analyze the clinical characteristics and prognostic survival of patients with neuroendocrine tumors of the thymus (NETTs), and to develop and validate a nomogram model for predicting the prognosis of patients.
We conducted a retrospective analysis of patients with neuroendocrine tumors of the thymus in the Surveillance, Epidemiology, and End Results (SEER) database in the United States between 1988 and 2016. Cox scale risk regression analysis, the Kaplan-Meier method and log-rank test were used to carry out the significance test to determine the independent prognostic factors, from which a nomogram for NETTs was established. C-index and calibration curve were used to evaluate the prediction accuracy of the model. External validation of the nomogram was performed using data from our center.
A total of 254 patients with NETTs were collected in the SEER database. In the multivariable analysis, T stage, tumor grade, surgery, and chemotherapy were found to be independent factors affecting the prognosis of patients (all P < 0.05). A nomogram model was constructed based on these variables, and its c-index was 0.707 (0.661-0.752). The c-index results showed that the nomogram model had better authentication capability than the eighth edition of the tumor, node, metastasis (TNM) staging system and Masaoka-Koga (MK) staging system. The calibration curve showed that the model could accurately predict patient prognosis.
The study established a nomogram model that predicted the overall survival rate of one-, three- and five-years, and used the survival prediction model to optimize individualized therapy and prognostic follow-up through risk stratification.
本研究旨在分析胸腺神经内分泌肿瘤(NETTs)患者的临床特征和预后生存情况,并建立和验证预测患者预后的列线图模型。
我们对美国 1988 年至 2016 年间监测、流行病学和最终结果(SEER)数据库中胸腺神经内分泌肿瘤患者进行了回顾性分析。采用 Cox 比例风险回归分析、Kaplan-Meier 方法和对数秩检验进行显著性检验,确定独立预后因素,从而建立 NETTs 的列线图。C 指数和校准曲线用于评估模型的预测准确性。使用我们中心的数据进行列线图的外部验证。
SEER 数据库共收集了 254 例 NETTs 患者。多变量分析发现,T 分期、肿瘤分级、手术和化疗是影响患者预后的独立因素(均 P<0.05)。基于这些变量构建了一个列线图模型,其 C 指数为 0.707(0.661-0.752)。C 指数结果表明,该列线图模型比第八版肿瘤、淋巴结、转移(TNM)分期系统和 Masaoka-Koga(MK)分期系统具有更好的验证能力。校准曲线表明,该模型能够准确预测患者的预后。
本研究建立了一个预测 1 年、3 年和 5 年总生存率的列线图模型,并通过风险分层,利用生存预测模型优化个体化治疗和预后随访。