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Prognostic nomogram for patients with Nasopharyngeal Carcinoma incorporating hematological biomarkers and clinical characteristics

Predictive models for survival prediction in individual cancer patients following the tumor, node, and metastasis (TNM) staging system are limited. The survival rates of patients who share TNM stage diseases are diversified. Therefore, we established a nomogram in which hematological biomarkers and...

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Detalles Bibliográficos
Autores principales: Li, Jianpei, Chen, Shulin, Peng, Songguo, Liu, Yijun, Xing, Shan, He, Xia, Chen, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968847/
https://www.ncbi.nlm.nih.gov/pubmed/29805306
http://dx.doi.org/10.7150/ijbs.24374
Descripción
Sumario:Predictive models for survival prediction in individual cancer patients following the tumor, node, and metastasis (TNM) staging system are limited. The survival rates of patients who share TNM stage diseases are diversified. Therefore, we established a nomogram in which hematological biomarkers and clinical characteristics for predicting the overall survival (OS) of nasopharyngeal carcinoma (NPC) patients were incorporated. The clinicopathological and follow-up data of 690 NPC patients who were histologically diagnosed histologically at the Sun Yat-sen University Cancer Center between July 2007 and December 2011 were retrospectively reviewed. Data was randomly divided into primary (n = 460) and validation groups (n = 230). Cox regression analysis was used to identify prognostic factors for building the nomogram in primary cohorts. The predictive accuracy and discriminative ability of the nomogram were measured by the concordance index (C-index) and decision curve, and were compared with the TNM staging system, Epstein-Barr virus DNA copy numbers (EBV DNA), or TMN stage plus EBV DNA. The results were internally validated by assessment of discrimination and calibration using the validation cohorts at the same institution. Independent factors selected into the nomogram for OS included age [hazard ratio (HR): 1.765; 95% confidence interval (CI): 1.008-3.090)], TNM stage (HR: 1.899; 95% CI: 1.023-3.525), EBV DNA (HR: 1.322; 95% CI: 1.087-1.607), lactate dehydrogenase level (LDH) (HR: 1.784; 95% CI: 1.032-3.086), high sensitivity C-reactive protein (hs-CRP) (HR: 1.840; 95% CI: 1.039-3.258), high-density lipoprotein cholesterol (HDL-C) (HR: 0.503; 95% CI: 0.282-0.896), hemoglobin (HGB) (HR: 0.539; 95% CI: 0.309-0.939) and lymphocyte to lymphocyte ratio (LMR) (HR:0.531; 95% CI: 0.293-0.962). The C-index in the primary cohort and validation cohort were 0.800 and 0.831, respectively, and were statistically higher when compared to C-index values for TNM stage (0.672 and 0. 716), EBV DNA (0.668 and 0.688), and TNM stage+ EBV DNA (0. 732 and 0. 760), P < 0.001 for all. Moreover, the decision curve analyses demonstrated that the nomogram model had a higher overall net benefit compared to the TNM staging system, EBV DNA and TNM stage+ EBV DNA. Next, patients were divided into three distinct risk groups for OS based on total points (TPs) of the nomogram: a low-risk group (TPs ≤ 19.0), an intermediate risk group (19.0 < TPs ≤ 25.5) and a high risk group (TPs > 25.5), respectively. The nomogram predicting prognosis generated for NPC patients had a higher predictive power compared to the TNM staging system, EBV DNA, and TNM stage+ EBV DNA.