ArticleRadiation oncology (London, England)2025
Prognostic nomogram for synchronous metastatic nasopharyngeal carcinoma: a retrospective multicentre study.
Article in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Machine learning-powered audio-omics processing method as an auxiliary diagnostic approach for advanced nasopharyngeal carcinoma.BMC cancer · 2026Article
- A prognostic nomogram integrating Epstein-Barr virus DNA and specific inflammatory indices for early recurrent or metastatic nasopharyngeal carcinoma after first-line nedaplatin-based therapy.Discover oncology · 2026Article
- Integrated primary tumor and all metastatic lymph nodes radiomics from 18 F-FDG PET/CT for precise M1 subclassification in nasopharyngeal carcinoma.BMC cancer · 2026Article
- Predicting overall survival in synchronous metastatic nasopharyngeal carcinoma using a stacking ensemble machine learning model: a multicenter retrospective study.Frontiers in oncology · 2026Article
- Development and validation of a machine learning model for predicting 3-year overall survival in metastatic nasopharyngeal carcinoma: a SEER database and web visualization study.Translational cancer research · 2025Article
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Abstract
backgroundPatients with synchronous metastatic nasopharyngeal carcinoma (smNPC) exhibit significant heterogeneity, and clinical prognostic models suitable for this cohort remain limited. We aimed to develop a prognostic prediction tool to facilitate personalised prognostic assessments and inform treatment decisions for these patients.
methodsThis retrospective multicentre study enrolled 556 patients with smNPC. The training cohort comprised 386 patients from Guangxi Medical University Cancer Hospital, while the external validation cohort comprised 170 patients from Wuzhou Red Cross Hospital and Xiangtan Central Hospital. We applied the Cox proportional hazards model to determine factors associated with overall survival (OS). A nomogram prognostic model was developed to predict OS based on the identified prognostic factors. The model's predictive performance was evaluated for discrimination and calibration, and patients were stratified based on their calculated prognostic risk scores. Kaplan-Meier survival curves were employed to assess prognostic differences across the stratified groups.
resultsMultivariate analysis identified that M classification, primary tumour radiotherapy, and immunotherapy were significantly associated with OS. A prognostic nomogram integrating these variables exhibited good discrimination (C-index: 0.743) and calibration, which was validated in an external validation cohort. Patients stratified by the model-derived risk scores into high- and low-risk groups showed a significant difference in survival disparity.
conclusionsWe established a nomogram prognostic model that effectively facilitated individualised prognostic prediction and risk stratification in patients with smNPC, thereby assisting clinicians in treatment decision-making.
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