Trial reportJournal of cancer research and clinical oncology2023
Two web-based dynamically interactive nomograms and risk stratification systems for predicting survival outcomes and guiding treatment in non-metastatic nasopharyngeal carcinoma.
Trial report in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Development of machine learning models for survival prediction in nasopharyngeal carcinoma using population-based data.Discover oncology · 2025Article
- Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.Frontiers in oncology · 2024Article
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2 authors.
Funding
Abstract
backgroundA nomogram is a valuable and easily accessible tool for individualizing cancer prognosis. This study aims to establish and validate two prognostic nomograms for long-term overall survival (OS) and cancer-specific survival (CSS) in non-metastatic nasopharyngeal carcinoma (NPC) patients and to investigate the treatment options for the nomogram-based risk stratification subgroups.
methodsA total of 3959 patients with non-metastatic NPC between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated to the training and validation cohorts in a 7:3 ratio. Prognostic nomograms were constructed to estimate OS and CSS by integrating significant variables from multivariate Cox regression employing a backward stepwise method. We examined the correlation indices (C-index) and areas under the curves (AUC) of time-dependent receiver operating characteristic curves to assess the discriminative ability of our survival models. The comprehensive enhancements of predictive performance were evaluated with net reclassification operating improvement (NRI) and integrated discrimination improvement (IDI). Reliability was validated using calibration plots. Decision curve analysis (DCA) was used to estimate clinical efficacy and capability. Finally, the nomogram-based risk stratification system used Kaplan-Meier survival analysis and log-rank tests to examine differences between subgroups.
resultsThe following independent parameters were significant predictors for OS: sex, age, race, marital status, histological type, median household income, AJCC stage tumor size, and lymph node size. Except for the race variables mentioned above, the rest were independent prognostic factors for CSS. The C-index, AUC, NRI, and IDI indicated satisfactory discriminating properties. The calibration curves exhibited high concordance with the exact outcomes. Moreover, the DCA demonstrated performed well for net benefits. The prognosis significantly differed between low- and high-risk patients (p < 0.001). In a treatment-based stratified survival analysis in risk-stratified subgroups, chemotherapy benefited patients in the high-risk group compared to radiotherapy alone. Radiotherapy only was recommended in the low-risk group.
conclusionsOur nomograms have satisfactory performance and have been validated. It can assist clinicians in prognosis assessment and individualized treatment of non-metastatic NPC patients.
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