Observational studyInternational journal of radiation oncology, biology, physics2021
Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy: Large-Scale Observational Cohort.
Observational study in International journal of radiation oncology, biology, physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- Osteoradionecrosis - an update for the general dental practitioner.British dental journal · 2026Review
- Personalized Prevention of Osteoradionecrosis of the Jaws: From Clinical Risk Profiling and Site-Specific Dosimetry to Artificial Intelligence and Individualized Treatment-Effect Estimation.Journal of personalized medicine · 2026Review
- A model-based selection of oral cancer patient for passive scattering proton beam therapy.Journal of radiation research · 2026Article
- Variable-interval temporal feathering to optimize organs-at-risk repair for head and neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Multi-institutional Normal Tissue Complication Probability (NTCP) Prediction Model for Mandibular Osteoradionecrosis: Results from the PREDMORN Study.International journal of radiation oncology, biology, physics · 2026Article
- HYpofractionated, dose-redistributed RAdiotherapy (HYDRA) versus conventional radiotherapy for head and neck cancer: planned interim analysis and dosimetric comparison from the phase I HYDRA trial.Clinical and translational radiation oncology · 2026Article
- Timing for Radiotherapy Initiation After Dental Extraction and Risk of Osteoradionecrosis in Head and Neck Cancer Patients: A Systematic Review and Meta-Analysis.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Machine Learning Models for Individualized Osteoradionecrosis Risk Prediction in Head and Neck Cancer.Journal of medical systems · 2026Article
- Clinical and dosimetric dataset of time-to-event normal tissue complication probability for osteoradionecrosis.Scientific data · 2026Article
- Beyond the dose-volume histogram: a critical appraisal of normal tissue complication probability modelling for osteoradionecrosis of the jaw and a strategic framework for clinical translation.Frontiers in oncology · 2026Article
- Advancing Normal Tissue Complication Probability Modeling with Supervised Contrastive Learning for Predicting Osteoradionecrosis.Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing · 2026Article
- Radiation-specific Automated Dosimetric dental, Mandible, and maxilla Annotation for Predicting Periodontal Problems (RADMAP): A semi-automated tool for dosimetric oral risk communication and osteoradionecrosis assessment.medRxiv : the preprint server for health sciences · 2025Article
- A semi-automated workflow for cohort-wise preparation of radiotherapy data for dose-response modeling, including autosegmentation of organs at risk.Journal of applied clinical medical physics · 2025Article
- Externally validated digital decision support tool for time-to-osteoradionecrosis risk-stratification using right-censored multi-institutional observational cohorts.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025Observational
- International Expert-Based Consensus Definition, Classification Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Interdisciplinary Modified Delphi Study.International journal of radiation oncology, biology, physics · 2025Article
- Predictive modelling of mandibular osteoradionecrosis in head and neck cancer patients: clinical and dosimetric insights.Clinical oral investigations · 2025Article
- Cost-effectiveness of personalized policies for implementing organ-at-risk sparing adaptive radiation therapy in head and neck cancer.Physics and imaging in radiation oncology · 2025Article
- Article
- A review of interaction of radiotherapy and implants in head and neck cancer - implications and strategies to improve outcomes.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeOsteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORN METHODS AND MATERIALS: Included patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset.
resultsOf 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORN
conclusionsThis study presented NTCP models based on mandible bone D
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Registered trials
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.