ArticleDiscover oncology2025
Risk factors and prediction models for severe radiation-induced oral mucositis in patients with nasopharyngeal carcinoma undergoing chemoradiotherapy.
Article in Discover oncology, 2025. 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.
- High-Recall Biomedical Language Models for Radiation Oncology Evidence Synthesis: An Integrated Systematic Review of Prediction Models for Radiotherapy-Induced Complications in Nasopharyngeal Carcinoma.Life (Basel, Switzerland) · 2026Review
- Regimen-Related Oral Mucosal Injury: Pathobiology, Clinical Consequences and Rationale for Intervention Across Cancer Treatment Modalities.Current oncology reports · 2026Review
- Genetic associations of LXN polymorphisms with toxicities of platinum-based concurrent chemoradiotherapy of nasopharyngeal carcinoma.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
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Authors and funding
4 authors.
Funding
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Abstract
objectiveThis study aimed to investigate the factors associated with severe radiation-induced oral mucositis (SRIOM) in nasopharyngeal carcinoma (NPC) patients undergoing chemoradiotherapy (CRT) and to establish a prediction model for SRIOM.
methodsA total of 262 NPC patients who underwent CRT were analyzed retrospectively, including 192 in the modeling group and 70 in the validation group. The modeling group was divided into the non-SRIOM group (n = 112) and the SRIOM group (n = 80), and the validation group was divided into the non-SRIOM group (n = 40) and the SRIOM group (n = 30) according to the presence of SRIOM. Univariate and multivariate logistic logistic analyses were performed on the clinical data and general characteristics of all patients to construct a prediction model for SRIOM in NPC patients. The practical efficacy of the prediction model was evaluated using Hosmer-Lemeshow test, receiver operating characteristic curve (ROC), and decision curve analysis (DCA).
resultsBMI < 23.9 kg/m
conclusionThe SRIOM prediction model, developed from the clinical characteristics and general information of NPC patients, is beneficial in clinical practice for identifying high-risk SRIOM and creating tailored treatment plans.
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