ArticleOral oncology2025
Evaluation of a comprehensive set of normal tissue complication probability models for patients with head and neck cancer in an international cohort.
Article in Oral oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Effectiveness of mHealth Interventions for Rehabilitation in Patients With Head and Neck Cancer: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR mHealth and uHealth · 2026Pooled it
- Digital Twins for Targeted Therapy in Head and Neck Cancer: From Molecular Stratification to Resistance-Aware Combination Strategies.Current oncology (Toronto, Ont.) · 2026Review
- Circulating Cell-free DNA as a biomarker for radiation-induced injury: From mechanistic insights to clinical translation.Cancer metastasis reviews · 2026Review
- Normal tissue complication probability modeling of radiation-induced neck fibrosis in head and neck Cancer survivors: a patient-reported outcome-based analysis.Clinical and translational radiation oncology · 2026Article
- A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025Article
- Validated prediction of xerostomia in a real-world population: a step toward model-guided radiotherapy.Acta oncologica (Stockholm, Sweden) · 2025Article
- Strategy to select nasopharyngeal cancer patients for adaptive radiotherapy.Frontiers in oncology · 2025Article
- Machine Learning Improves Prediction of Normal-Tissue Complications Compared with Radiobiological Models in Head-and-Neck Radiotherapy: An Exploratory Small-cohort Study.Journal of medical physicsArticle
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19 authors.
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Abstract
background/purposeNormal tissue complication probability (NTCP) models can be used to guide radiation therapy (RT) decisions by estimating side-effect risks pretreatment to minimize (late) side-effects. Recently, a comprehensive individual toxicity risk (CITOR) profile of NTCP models addressing common side-effects in head and neck cancer (HNC) patients was developed. This study investigates the generalizability of these models in an international setting, with different treatment approaches and side-effect assessments, promoting their integration into more widespread clinical practice. MATERIALS/
methodsFrom a prospective registry study, 407 HNC patients were included who were treated with definitive RT with or without systemic therapy between 2015 and 2022. NTCP models predicting dysphagia, aspiration, xerostomia, sticky saliva, taste loss, speech problems, oral pain, and fatigue at 6 and 12 months after RT were evaluated. All side-effects were patient-rated using the MDASI-HN, except dysphagia which was reported by clinicians using the PSS-HN diet normalcy score. Model performance was appraised by discrimination (area under the curve [AUC]) and calibration.
resultsCITOR models showed moderate-to-high performance in this cohort (mean AUC = 0.67[range = 0.55-0.80], moderate-to-good calibration). NTCP models for dysphagia, xerostomia, sticky saliva, and fatigue were the top performing models. Models for aspiration, taste loss and speech problems performed moderately well, which was partly explained by lower incidences.
conclusionDespite differences between the CITOR development and this evaluation cohort, including use of different side-effect scoring systems, most models exhibited moderate-to-high performance. This demonstrated that the dose-effect relations were generalizable. Therefore, this study supports further integration of these NTCP models in clinical practice.
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