ArticleCurrent oncology (Toronto, Ont.)2022
Predicting Nomogram for Severe Oral Mucositis in Patients with Nasopharyngeal Carcinoma during Intensity-Modulated Radiation Therapy: A Retrospective Cohort Study.
Article in Current oncology (Toronto, Ont.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Prediction models of severe radiation-induced oral mucositis: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- Effect of modified Baduanjin exercise on nutritional status in patients with nasopharyngeal carcinoma: a randomized controlled trial.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Trial
- 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
- Evaluation of mid-treatmentQuantitative imaging in medicine and surgery · 2026Article
- Article
- Construction and Validation of a Nomogram Prediction Model for Individualized Prediction of the Risk of Oral Infection in Patients with Nasopharyngeal Carcinoma Undergoing Radiotherapy and Chemotherapy.Cancer management and research · 2026Article
- Machine learning-based prediction of severe oral mucositis from head and neck cancer radiation therapy.Radiation oncology journal · 2025Article
- Integrating dose-volume histogram parameters and radiomics-based machine learning to identify carbon ion radiotherapy-induced acute oral mucositis in patients with head and neck cancer.Physics and imaging in radiation oncology · 2025Article
- Improved Efficacy of a Predictive Model for Swallowing-Induced Breakthrough Pain Based on a Redefined Delineation Method in Locally Advanced Nasopharyngeal Carcinoma.Advances in radiation oncology · 2025Article
- A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma.La Radiologia medica · 2025Article
- Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Oral dose-volume parameters as independent predictors of severe radiation-induced mucositis in nasopharyngeal carcinoma patients: a retrospective study.American journal of translational research · 2025Article
- Increased acid sphingomyelinase levels in saliva as oral mucositis severity predictors.Frontiers in oncology · 2025Article
- Adverse drug reactions and drug-related problems with supportive care medications among the oncological population.Discover oncology · 2024Article
- Microbial metabolites affect tumor progression, immunity and therapy prediction by reshaping the tumor microenvironment (Review).International journal of oncology · 2024Review
- Article
- Predictive Factors for Chemoradiation-Induced Oral Mucositis and Dysphagia in Head and Neck Cancer: A Scoping Review.Cancers · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral mucositis is an acute adverse reaction with high incidence during radiotherapy. Severe oral mucositis can seriously affect patients' quality of life and compliance with radiotherapy. The aim of this study was to identify the risk factors for severe oral mucositis and to develop a nomogram for predicting severe oral mucositis in patients with nasopharyngeal carcinoma.
methodsOne hundred and ninety patients with nasopharyngeal carcinoma were retrospectively screened in this study. Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were performed to identify the best predictors of severe oral mucositis. A nomogram was constructed based on the factors. Finally, the discriminative ability of the nomogram was evaluated.
resultsFour independent factors predicting severe oral mucositis were identified: age, N stage, the cycle of induction chemotherapy, and dose-volumetric parameter V40 (%) of oral cavity. The area under the receiver of operating characteristic curve of the nomogram was 0.759 (95% confidence interval: 0.691-0.827).
conclusionsA predictive nomogram for severe oral mucositis was established and validated in this study. The nomogram provides a reliable and practical model for clinically predicting the probability of severe oral mucositis in patients with nasopharyngeal carcinoma before intensity-modulated radiation therapy.
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