Prediction of radiation-induced mucositis of H&N cancer patients based on a large patient cohort-Reference-Cited by-同舟云学术

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Prediction of radiation-induced mucositis of H&N cancer patients based on a large patient cohort

Published:2020-06 Issue: Volume:147 Page:15-21
ISSN:0167-8140
Container-title:Radiotherapy and Oncology
language:en
Short-container-title:Radiotherapy and Oncology

Author:

Hansen C.R.,Bertelsen A.,Zukauskaite R.,Johnsen L.,Bernchou U.,Thwaites D.I.,Eriksen J.G.,Johansen J.,Brink C.^ORCID

Funder

Danish Cancer Society grant

University of Southern Denmark scholarship

Odense University Hospital scholarship

Danish Cancer Research Fund

Odense University Hospital

Publisher

Elsevier BV

Subject

Radiology Nuclear Medicine and imaging,Oncology,Hematology

Reference28 articles.

1. Predictive modelling for swallowing dysfunction after primary (chemo)radiation: results of a prospective observational study;Christianen;Radiother Oncol,2012

2. NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: the role of dosimetric and clinical factors;Beetz;Radiother Oncol,2012

3. Automatic treatment planning improves the clinical quality of head and neck cancer treatment plans;Hansen;Clin Transl Radiat Oncol,2016

4. 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;Dean;Radiother Oncol,2016

5. Normal tissue complication probability (NTCP) modelling of severe acute mucositis using a novel oral mucosal surface organ at risk;Dean;Clin Oncol (R Coll Radiol),2017

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1. Comment on: The relevance of dental management prior to radiation therapy with severe oral mucositis in head and neck cancer patients;Journal of Dental Sciences;2024-10

2. Acute toxicities in proton therapy for head and neck cancer – A matched analysis of the DAHANCA 35 feasibility study;Clinical and Translational Radiation Oncology;2024-09

3. Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning;Clinical and Translational Radiation Oncology;2024-09

4. 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;2024-08

5. Interobserver variation in organs at risk contouring in head and neck cancer according to the DAHANCA guidelines;Radiotherapy and Oncology;2024-08

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