ArticleCancer cell international2021
Development and validation of a risk prediction score for patients with nasopharyngeal carcinoma.
Article in Cancer cell international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development of machine learning models for survival prediction in nasopharyngeal carcinoma using population-based data.Discover oncology · 2025Article
- Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.World journal of surgical oncology · 2025Article
- A nomogram with the peripheral blood neutrophil-to-lymphocyte ratio before treatment predicts the survival of patients with nasopharyngeal carcinoma.Frontiers in oncology · 2025Article
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8 authors.
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Abstract
backgroundWe aimed to develop and validate a predictive model for the overall survival (OS) of patients with nasopharyngeal carcinoma (NPC).
methodsOverall, 519 patients were retrospectively reviewed in this study. In addition, a random forest model was used to identify significant prognostic factors for OS among NPC patients. Then, calibration plot and concordance index (C-index) were utilized to evaluate the predictive accuracy of the nomogram model.
resultsWe used a random forest model to select the three most important features, dNLR, HGB and EBV DNA, which were significantly associated with the OS of NPC patients. Furthermore, the C-index of our model for OS were 0.733 (95% CI 0.673 ~ 0.793) and 0.772 (95% CI 0.691 ~ 0.853) in the two cohorts, which was significantly higher than that of the TNM stage, treatment, and EBV DNA. Based on the model risk score, patients were divided into two groups, associated with low-risk and high-risk. Kaplan-Meier curves demonstrated that the two subgroups were significantly associated with OS in the primary cohort, as well as in the validation cohort. The nomogram for OS was established using the risk score, TNM stage and EBV DNA in the two cohorts. The nomogram achieved a higher C-index of 0.783 (95% CI 0.730 ~ 0.836) than that of the risk score model 0.733 (95% CI 0.673 ~ 0.793) in the primary cohort (P = 0.005).
conclusionsThe established risk score model and nomogram resulted in more accurate prognostic prediction for individual patient with NPC.
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