A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Cancer Medicine

Changchun Lai¹  Chuchan Zhou¹  Xia Ke¹  Chunning Zhang²  Lei Zhou³  Hualiang Lv⁴  Hanqing Huang⁵  Hao Chen⁶  Shulin Chen⁶ 

[1] Department Of Clinical Laboratory Maoming People's Hospital Maoming P. R. China;Department Of First Tumor Maoming People's Hospital Maoming P. R. China;Department Of Pathology Laboratory Maoming People's Hospital Maoming P. R. China;Department of Pulmonary and Critical Care Medicine Maoming People's Hospital Maoming P. R. China;Department of Thoracic Surgery Maoming People's Hospital Maoming P. R. China;State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat‐sen University Cancer Center Guangzhou P. R. China;

关键词: lasso regression;    model;    nasopharyngeal carcinoma;    nomogram;    prognostic;   

DOI  :  10.1002/cam4.3839

来源: DOAJ

【 摘 要 】

Abstract This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model's incremental value to the TNM staging system, clinical treatment, and Epstein‐Barr virus (EBV) DNA copy number for individual OS estimation. We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort. Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C‐index), time‐dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high‐risk and low‐risk groups according to the model risk score, and we analyzed the survival time of these two groups using Kaplan–Meier survival plots. All results were validated in the independent validation cohort. Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C‐index, tdROC, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low‐risk (risk score ≤ −1.423) and high‐risk (risk score > −1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort. The proposed novel prognostic model based on clinical features and serological markers may represent a promising tool for estimating OS in NPC patients.

【 授权许可】

Unknown