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Updated: Sep 15, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A Practical Online Dynamic Nomogram to Predict the Progression-Free Survival in Nasopharyngeal Carcinoma
Jiayan Zhang1,2, Jiayi Yu1, Dan Zhang1
1Department of Radiology, Chongqing General Hospital, Chongqing, China.
Objective:
This retrospective study aimed to establish a convenient and effective online dynamic nomogram for predicting progression-free survival (PFS) in nasopharyngeal carcinoma (NPC).
Methods:
The clinical and imaging characteristics were retrospectively collected from 106 patients with pathologically confirmed NPC. Univariate and multivariate Cox proportional hazards regression analyses were performed to select the independent prognostic factors and construct a nomogram for predicting 1-, 3-, and 5-year PFS. The predictive effectiveness and clinical utility of the nomogram were evaluated by concordance index (C-index), calibration curves, and decision curve analysis (DCA). Patients were divided into different groups by risk score based on the nomogram, and the PFS rates of these two groups were compared by Kaplan-Meier curves.
Results:
Univariate and multivariate analyses indicated that the ADC (OR 0.177, 95% CI 0.068-0.464), extranodal neoplastic spread (ENS) (OR 3.662, 95% CI 1.495-8.968), and lymphocyte-to-monocyte ratio (LMR) (OR 2.688, 95% CI 1.094-6.607) at baseline were independent prognostic factors of NPC, with all p < 0.05. The nomogram revealed favourable predictive performance (C-index = 0.795). The area under the receiver operating characteristic curve (AUC) of the nomogram for predicting 1-, 3-year, and 5-year PFS was 0.792, 0.849, and 0.822, respectively, which outperformed the AJCC 8th TNM staging system (AUC = 0.592, 0.543, and 0.575). Besides, the nomogram distinguished the PFS rates well between low-and high-risk groups (p < 0.0001).
Conclusion:
The online dynamic nomogram based on ADC, ENS, and LMR can divide NPC patients into different risk groups, and its prediction efficiency is better than the TNM stage system.
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