ArticleEuropean 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 Surgery2023
Development and validation of a model for the prediction of disease-specific survival in patients with oral squamous cell carcinoma: based on random survival forest analysis.
Article in 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, 2023. 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.
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Evolution of oral and oropharyngeal cancer in the Basque Country (Spain) from 1986-1994 to 2012-2017.Medicina oral, patologia oral y cirugia bucal · 2025Article
- A prognostic model for head and neck squamous cell carcinoma based on radiotherapy sensitivity insights from nasopharyngeal carcinoma.Discover oncology · 2025Article
- Overcoming Resistance to Standard-of-Care Therapies for Head and Neck Squamous Cell Carcinomas.Cells · 2024Review
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Authors and funding
13 authors.
Funding
Abstract
objectiveTo establish a model for predicting the disease-specific survival (DSS) of patients with oral squamous cell carcinoma (OSCC).
methodsPatients diagnosed with OSCC from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled and randomly divided into development (n = 14,495) and internal validation cohort (n = 9625). Additionally, a cohort from a hospital located in Southeastern China was utilized for external validation (n = 582).
resultsTNM stage, adjuvant treatment, surgery, tumor sites, age, grade, and gender were used for RSF model construction based on the development cohort. The effectiveness of the model was confirmed through time-dependent ROC curves in different cohorts. The risk score exhibited an almost exponential increase in the hazard ratio of death due to OSCC. In development, internal, and external validation cohorts, the prognosis was significantly worse for patients in groups with higher risk scores (all log-rank P < 0.05).
conclusionBased on RSF, a high-performance prediction model for OSCC prognosis was created and verified in this study.
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