ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2016
Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 6 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 6 syntheses or guidelines pooled it, 111 citations in OpenAlex.
- Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- 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
- Expert consensus on the prevention and treatment of radiochemotherapy-induced oral mucositis.International journal of oral science · 2025Guideline
- Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.Radiation oncology (London, England) · 2024Pooled it
- Systematic Review of Normal Tissue Complication Models Relevant to Standard Fractionation Radiation Therapy of the Head and Neck Region Published After the QUANTEC Reports.International journal of radiation oncology, biology, physics · 2018Pooled it
- Variable-interval temporal feathering to optimize organs-at-risk repair for head and neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Evaluation of mid-treatmentQuantitative imaging in medicine and surgery · 2026Article
- Article
- Benchmarking of radiobiological NTCP models in head and neck radiotherapy using independent computational pipelines: an institutional validation study with machine learning augmentation.Ecancermedicalscience · 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
- Data-centric artificial intelligence and cancer research: construction of a real-world head and neck treatment data repository.ESMO real world data and digital oncology · 2025Article
- A risk model for predicting partial late radiation-induced injuries in T3-4 nasopharyngeal carcinoma based on dosimetric parameters and clinical factors.Discover oncology · 2025Article
- A multi-center, multi-organ, multi-omic prediction model for treatment-induced severe oral mucositis in nasopharyngeal carcinoma.La Radiologia medica · 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
- Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity.Advances in radiation oncology · 2025Article
- Impact of clinical and dosimetric factors on severe oral mucositis in head and neck cancer: insights from a phase II clinical trial.Frontiers in oncology · 2025Article
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.International journal of radiation oncology, biology, physics · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
background and purposeSevere acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning. MATERIALS AND
methodsPredictive models of severe acute mucositis were generated using radiotherapy dose (dose-volume and spatial dose metrics) and clinical data. Penalised logistic regression, support vector classification and random forest classification (RFC) models were generated and compared. Internal validation was performed (with 100-iteration cross-validation), using multiple metrics, including area under the receiver operating characteristic curve (AUC) and calibration slope, to assess performance. Associations between covariates and severe mucositis were explored using the models.
resultsThe dose-volume-based models (standard) performed equally to those incorporating spatial information. Discrimination was similar between models, but the RFCstandard had the best calibration. The mean AUC and calibration slope for this model were 0.71 (s.d.=0.09) and 3.9 (s.d.=2.2), respectively. The volumes of oral cavity receiving intermediate and high doses were associated with severe mucositis.
conclusionsThe RFCstandard model performance is modest-to-good, but should be improved, and requires external validation. Reducing the volumes of oral cavity receiving intermediate and high doses may reduce mucositis incidence.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.