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

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.

Keywords: lasso regression; model; nasopharyngeal carcinoma; nomogram; prognostic.

FIGURE 1

Potential predictors' selection using LASSO regression model

FIGURE 2

Comparison of predictive accuracy between prognostic model, TNM staging, and clinical treatment using time‐dependent ROC curves in training cohort and validation cohort

FIGURE 3

Decision curve analysis for each model in training cohort and validation cohort

FIGURE 4

The nomogram was used to estimate OS for NPC patients in the training cohor

FIGURE 5

The calibration plot for the nomograms at 1‐, 3‐, 5‐ year in the training cohort

FIGURE 6

The optimal cut‐off value of prognostic model risk score using R package "survival," and the distribution of the prognostic model risk score in the training cohort and validation cohort

FIGURE 7

Kaplan–Meier analyses of OS according to the prognostic model risk score classifier in subgroups of NPC patients in the training cohort and the validation cohort