Table_1_Nomograms Forecasting Long-Term Overall and Cancer Specific Survival of Patients With Head and Neck Neuroendocrine Carcinoma.docx

BackgroundThe purpose of this retrospective analysis was to build and validate nomograms to predict the cancer-specific survival (CSS) and overall survival (OS) of head and neck neuroendocrine carcinoma (HNNEC) patients.
MethodsA total of 493 HNNEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and 74 HNNEC patients were collected from the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital (HCH) between 2008 and 2020. Patients from SEER were randomly assigned into training (N=345) and internal validation (N=148) groups, and the independent data group (N=74) from HCH was used for external validation. Independent prognostic factors were collected using an input method in a Cox regression model, and they were then included in nomograms to predict 3‐, 5‐, and 10‐year CSS and OS rates of HNNEC patients. Finally, we evaluated the internal and external validity of the nomograms using the consistency index, while assessing their prediction accuracy using calibration curves. A receiver operating curve (ROC) was also used to measure the performance of the survival models.
ResultsThe 3-, 5-, and 10-year nomograms of this analysis demonstrated that M classification had the largest influence on CSS and OS of HNNEC, followed by the AJCC stage, N stage, age at diagnosis, sex/gender, radiation therapy, and marital status. The training validation C-indexes for the CSS and OS models were 0.739 and 0.713, respectively. Those for the internal validation group were 0.726 and 0.703, respectively, and for the external validation group were 0.765 and 0.709, respectively. The area under the ROC curve (AUC) of 3-, 5-, and 10-year CSS and OS models were 0.81, 0.82, 0.82, and 0.78, 0.81, and 0.82, respectively. The C-indexes were all higher than 0.7, indicating the high accuracy ability of our model’s survival prediction.
ConclusionsIn this study, prognosis nomograms in HNNEC patients were constructed to predict CSS and OS for the first time. Clinicians can identify patients’ survival risk better and help patients understand their survival prognosis for the next 3, 5, and 10 years more clearly by using these nomograms.

背景：本回顾性分析旨在构建并验证列线图，以预测头颈部神经内分泌癌（head and neck neuroendocrine carcinoma, HNNEC）患者的癌症特异性生存（cancer-specific survival, CSS）与总生存（overall survival, OS）情况。
方法：本研究共纳入2004年至2015年来自监测、流行病学与最终结果（Surveillance, Epidemiology, and End Results, SEER）数据库的493例头颈部神经内分泌癌患者，以及2008年至2020年来自中南大学湘雅医学院附属肿瘤医院/湖南省肿瘤医院（Hunan Cancer Hospital, HCH）的74例头颈部神经内分泌癌患者。SEER数据库来源患者被随机分为训练集（N=345）与内部验证集（N=148），而来自HCH的74例独立患者组成的队列作为外部验证集。本研究采用Cox回归模型的变量纳入法筛选独立预后因素，并将其纳入列线图，以预测头颈部神经内分泌癌患者的3年、5年及10年癌症特异性生存率与总生存率。最后，本研究采用一致性指数（C-index）评估列线图的内部与外部效度，并通过校准曲线评估其预测准确性；同时采用受试者工作特征曲线（receiver operating characteristic, ROC）分析生存模型的预测性能。
结果：本研究构建的3年、5年及10年列线图显示，M分期对头颈部神经内分泌癌患者的癌症特异性生存与总生存影响最大，其次依次为AJCC分期（AJCC stage）、N分期、确诊年龄、性别、放射治疗与婚姻状况。癌症特异性生存模型与总生存模型的训练集一致性指数分别为0.739与0.713；内部验证集的一致性指数分别为0.726与0.703；外部验证集的一致性指数分别为0.765与0.709。3年、5年及10年癌症特异性生存模型的受试者工作特征曲线下面积（area under the ROC curve, AUC）分别为0.81、0.82、0.82，总生存模型的对应值分别为0.78、0.81、0.82。所有一致性指数均高于0.7，表明本模型的生存预测具有较高准确性。
结论：本研究首次构建了用于预测头颈部神经内分泌癌患者癌症特异性生存与总生存的预后列线图。临床医师可通过该列线图更好地识别患者的生存风险，并帮助患者更清晰地了解其未来3年、5年及10年的生存预后情况。