未经证实:胃肠胰腺神经内分泌癌(GEP-NECs)是一种罕见的,胃肠胰腺神经内分泌肿瘤(GEP-NENs)的高度恶性子集。然而,如何通过临床特征预测GEP-NECs的预后仍在研究中。本研究旨在建立并验证GEP-NEC患者的总体生存(OS)的列线图模型,以预测其预后。
未经证实:我们从监测中选择诊断为GEP-NEC的患者,流行病学,和最终结果(SEER)数据库和两家中国医院。随机化后,我们将SEER数据库中的数据以7:3的比例分为训练队列和测试队列,并使用中国队列作为验证队列.进行Cox单变量和多变量分析以将统计学上显著的变量并入列线图模型中。然后,我们建立了一个列线图,并通过一致性指数(C指数)对其进行了验证,校正曲线,接收机工作特性(ROC)曲线,曲线下面积(AUC),和决策曲线分析(DCA)曲线。
UNASSIGNED:我们计算了列线图C指数为0.797,列车队列中的95%置信区间(95%CI)为0.783-0.815,测试队列中的0.816(95%CI:0.794-0.833)和验证队列中的0.801(95%CI:0.784-0.827)。然后,我们绘制了校准曲线和ROC曲线,和AUC,以验证模型的特异性和敏感性,1-,3年和5年AUC分别为0.776、0.768和0.770,在训练队列中;在测试队列中0.794,0.808和0.799;在验证队列中0.922,0.925和0.947.校准曲线和DCA曲线也表明该列线图模型具有良好的临床益处。
UNASSIGNED:我们建立了GEP-NEC患者的OS列线图模型,包括年龄变量,种族,性别,肿瘤部位,肿瘤分级,TNM阶段。该模型具有良好的拟合,高灵敏度和特异性,和良好的临床效益。
UNASSIGNED: Gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs) are a rare, highly malignant subset of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). However, how to predict the prognosis of GEP-NECs by clinical features is still under study. This study aims to establish and validate a nomogram model of overall survival (OS) in patients with GEP-NECs for predicting their prognosis.
UNASSIGNED: We selected patients diagnosed with GEP-NECs from the Surveillance, Epidemiology, and End Results (SEER) database and two Chinese hospitals. After randomization, we divided the data in the SEER database into the train cohort and the test cohort at a ratio of 7:3 and used the Chinese cohort as the validation cohort. The Cox univariate and multivariate analyses were performed to incorporate statistically significant variables into the nomogram model. We then established a nomogram and validated it by concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, the area under the curve (AUC), and the decision curve analysis (DCA) curve.
UNASSIGNED: We calculated the nomogram C-index as 0.797 with a 95% confidence interval (95% CI) of 0.783-0.815 in the train cohort, 0.816 (95% CI: 0.794-0.833) in the test cohort and 0.801 (95% CI: 0.784-0.827) in the validation cohort. Then, we plotted the calibration curves and ROC curves, and AUCs were obtained to verify the specificity and sensitivity of the model, with 1-, 3- and 5-year AUCs of 0.776, 0.768, and 0.770, respectively, in the train cohort; 0.794, 0.808, and 0.799 in the test cohort; 0.922, 0.925, and 0.947 in the validation cohort. The calibration curve and DCA curves also indicated that this nomogram model had good clinical benefits.
UNASSIGNED: We established the OS nomogram model of GEP-NEC patients, including variables of age, race, sex, tumor site, tumor grade, and TNM stage. This model has good fitting, high sensitivity and specificity, and good clinical benefits.