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预测鼻咽癌患者根治性放疗后生存的列线图的开发与外部验证

Development and External Validation of Nomograms for Predicting Survival in Nasopharyngeal Carcinoma Patients after Definitive Radiotherapy.

作者信息

Yang Lin, Hong Shaodong, Wang Yan, Chen Haiyang, Liang Shaobo, Peng Peijian, Chen Yong

机构信息

Sun Yat-sen University cancer center, 651 Dongfeng Road east, Guangzhou, China.

State Key Laboratory of Oncology in Southern China, Guangzhou, China.

出版信息

Sci Rep. 2015 Oct 26;5:15638. doi: 10.1038/srep15638.

Abstract

The distant metastasis free survival (DMFS) and overall survival (OS) differ significantly among individuals even within the same clinical stages. The purpose of this retrospective study was to build nomograms incorporating plasma EBV DNA for predicting DMFS and OS of nasopharyngeal carcinoma (NPC) patients after definitive radiotherapy. A total of 1168 non-metastatic NPC patients from two institutions were included to develop the nomograms. Seven and six independent prognostic factors were identified to build the nomograms for OS and DMFS, respectively. The models were externally validated by a separate cohort of 756 NPC patients from the third institutions. For predicting OS, the c-index of the nomogram was significantly better than that of the TNM staging system (Training cohort, P = 0.005; validation cohort, P = 0.03). The c-index of nomogram for DMFS in the training and validation set were both higher than that of TNM classification with marginal significance (P = 0.048 and P = 0.057, respectively). The probability of 1-, 3-, and 5-year OS and DMFS showed optimal agreement between nomogram prediction and actual observation. The proposed stratification of risk groups based on the nomograms allowed significant distinction between Kaplan-Meier curves for survival outcomes. The prognostic nomograms could better stratify patients into different risk groups.

摘要

即使在相同临床分期的个体中,无远处转移生存期(DMFS)和总生存期(OS)也存在显著差异。本回顾性研究的目的是构建包含血浆EBV DNA的列线图,以预测鼻咽癌(NPC)患者根治性放疗后的DMFS和OS。来自两个机构的1168例非转移性NPC患者被纳入以构建列线图。分别确定了7个和6个独立预后因素以构建OS和DMFS的列线图。该模型通过来自第三个机构的756例NPC患者的独立队列进行外部验证。对于预测OS,列线图的c指数显著优于TNM分期系统(训练队列,P = 0.005;验证队列,P = 0.03)。训练集和验证集中DMFS列线图的c指数均高于TNM分类,具有边缘显著性(分别为P = 0.048和P = 0.057)。1年、3年和5年OS及DMFS的概率在列线图预测和实际观察之间显示出最佳一致性。基于列线图提出的风险组分层在生存结果的Kaplan-Meier曲线之间实现了显著区分。预后列线图可以更好地将患者分层到不同风险组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0795/4620487/60f87115e5ae/srep15638-f1.jpg

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