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    基于SEER数据库建立鼻咽癌预后预测模型列线图

    Establishment of a nomogram for the prognosis prediction model of nasopharyngeal carcinoma based on SEER database

    • 摘要: 目的 探究影响鼻咽癌患者预后的因素。方法 通过SEER*Stat软件提取SEER数据库中2010—2015年确诊的鼻咽癌患者临床资料。通过单因素和多因素 Cox回归分析,探讨影响鼻咽癌患者预后的临床因素,最终建立列线图来预测1年、3年和5年肿瘤特异性生存(CSS)率。通过一致性指数(C指数)、校准图、曲线下面积(AUC)和受试者工作特征(ROC)曲线来评估列线图的判别和预测能力。通过决策曲线分析(DCA)评估列线图的临床应用价值。结果 对367例鼻咽癌患者进行队列分析,多因素Cox回归分析显示,种族、婚姻状况、AJCC T、AJCC N、放疗、肝转移、诊断年龄与CSS独立相关,构建列线图预测模型。ROC曲线分析显示1年、3年、5年的总生存率AUC均大于0.65,内部和外部校准图显示列线图预测和观测结果之间有极好的一致性。DCA决策曲线显示列线图预测模型相对于放疗方案具有更好的临床益处。结论 列线图可以准确预测鼻咽癌患者的预后,具有很好的临床应用价值。

       

      Abstract: Objective To explore the prognostic factors of nasopharyngeal carcinoma (NPC) patients. Methods Clinical data were collected from patients who were diagnosed with NPC between 2010 and 2015 from SEER database using SEER*Stat software. Univariate and multivariate Cox regression analyses were used to explore the clinical factors affecting the prognosis of NPC patients. Finally, a nomogram was established to predict the 1-year, the 3- and 5-year cancer-specific survival (CSS) rate. The discriminatory and predictive capacities of the nomogram were assessed by concordance index (C-index), calibration plots, area under the curve (AUC) and time-dependent receiver operating characteristic (ROC) curve. The clinical use of nomogram was estimated by decision curve analysis (DCA). Results A cohort of 367 patients with NPC was analyzed. Multivariate analysis showed that race, marriage, AJCC T, AJCC N, radiation, liver metastasis, and age at diagnosis were independently associated with CSS. These factors were applied for the establishment of the nomogram. According to ROC curve, the 1-, 3-, and 5- year overall survival rates (AUC) were greater than 0.65, and internal and external calibration plots showed excellent agreement between nomogram prediction and observed outcomes. DCA analysis showed that the nomogram had better clinical benefits than radiotherapy. Conclusions The nomogram can accurately predict the prognosis of NPC patients, with good clinical application value.

       

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