Data Sheet 1_Dynamic nomogram for predicting the overall survival and cancer-specific survival of patients with gastrointestinal neuroendocrine tumor: a SEER-based retrospective cohort study and external validation.pdf

BackgroundGastrointestinal neuroendocrine tumor (GI-net) is a rare heterogeneous tumor, and there is a lack of models to predict its prognosis. Our study aims to develop and validate two new nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of GI-net patients and investigate their application value.
MethodsSEER*Stat 8.4.4 software was used to download clinicopathological information of GI-net patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training group (n=3007) and an internal-validation group (n=1289) at a 7:3 ratio. Patients from the Fourth Hospital of Hebei Medical University were enrolled in this study to form the external-validation group (n=86). Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. X-tile was used to divide GI-net patients into high-, medium-, and low-risk groups. Kaplan–Meier (KM) curves and log-rank tests were used to compare survival differences among the three groups.
ResultsSeven variables (age, site, size, grade, M stage, surgery, and chemotherapy) were selected to establish the nomogram for OS, and 6 variables (age, size, grade, M stage, surgery, and chemotherapy) were selected for CSS. The C indices (0.785, 0.813, and 0.936 in the training, internal-validation, and external-validation groups for OS; 0.888, 0.893, and 0.930 for CSS, respectively) and AUCs (≥0.7) indicated that the nomograms had satisfactory discriminative ability. Calibration curve analysis and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. KM curves indicated that each of the two nomograms clearly differentiated the high-, medium-, and low-risk groups. In addition, two online risk calculators were developed to predict the OS and CSS of these patients visually.
ConclusionsOur nomograms may play an important role in predicting 3- and 5-year OS and CSS for GI-net patients. Risk stratification systems and online risk calculators can be utilized in clinical practice to help doctors create personalized treatment plans.

背景
胃肠道神经内分泌肿瘤（Gastrointestinal neuroendocrine tumor, GI-net）是一类罕见的异质性肿瘤，目前尚缺乏用于预测其预后的模型。本研究旨在开发并验证两款新型列线图（nomogram），以预测胃肠道神经内分泌肿瘤患者的总生存期（Overall Survival, OS）与肿瘤特异性生存期（Cancer-specific Survival, CSS），并评估其临床应用价值。

方法
本研究通过SEER*Stat 8.4.4软件，从监测、流行病学与最终结果数据库（Surveillance, Epidemiology, and End Results database, SEER）中下载2010至2015年胃肠道神经内分泌肿瘤患者的临床病理资料。按照7:3的比例将患者随机分为训练集（n=3007）与内部验证集（n=1289）；另纳入河北医科大学第四医院的患者作为外部验证集（n=86）。通过单因素与多因素Cox分析筛选独立预后因子，并构建两款列线图。采用一致性指数（Concordance Index, C-index）、时间依赖性受试者工作特征曲线下面积（time-dependent receiver operating characteristic curve, AUC）、校准曲线以及决策曲线分析（Decision Curve Analysis, DCA）对列线图进行评价。使用X-tile软件将胃肠道神经内分泌肿瘤患者划分为高、中、低三个风险组；通过Kaplan-Meier（KM）曲线与log-rank检验比较三组患者的生存差异。

结果
用于预测总生存期的列线图纳入7项变量：年龄、发病部位、肿瘤大小、病理分级、M分期、手术治疗与化疗；用于预测肿瘤特异性生存期的列线图则纳入6项变量：年龄、肿瘤大小、病理分级、M分期、手术治疗与化疗。训练集、内部验证集与外部验证集中，总生存期对应的C指数分别为0.785、0.813与0.936，肿瘤特异性生存期对应的C指数分别为0.888、0.893与0.930；且两类列线图的AUC均≥0.7，提示其具备良好的区分能力。校准曲线分析与决策曲线分析结果显示，两款列线图对总生存期与肿瘤特异性生存期均具备令人满意的预测效能。Kaplan-Meier曲线结果表明，两款列线图均可清晰区分高、中、低风险组。此外，本研究还开发了两款在线风险计算器，可可视化预测患者的总生存期与肿瘤特异性生存期。

结论
本研究构建的列线图可有效预测胃肠道神经内分泌肿瘤患者的3年与5年总生存期及肿瘤特异性生存期。风险分层系统与在线风险计算器可应用于临床实践，辅助医师为患者制定个体化治疗方案。