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dc.contributor.authorDean, JA
dc.contributor.authorWong, KH
dc.contributor.authorWelsh, LC
dc.contributor.authorJones, A-B
dc.contributor.authorSchick, U
dc.contributor.authorNewbold, KL
dc.contributor.authorBhide, SA
dc.contributor.authorHarrington, KJ
dc.contributor.authorNutting, CM
dc.contributor.authorGulliford, SL
dc.date.accessioned2016-08-26T15:19:06Z
dc.date.issued2016-07
dc.identifier.citationRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016, 120 (1), pp. 21 - 27
dc.identifier.issn0167-8140
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/77
dc.identifier.eissn1879-0887
dc.identifier.doi10.1016/j.radonc.2016.05.015
dc.description.abstractBackground and purpose Severe 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 methods Predictive 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.Results The 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.Conclusions The 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.
dc.formatPrint-Electronic
dc.format.extent21 - 27
dc.languageeng
dc.language.isoeng
dc.subjectHumans
dc.subjectHead and Neck Neoplasms
dc.subjectStomatitis
dc.subjectRadiation Injuries
dc.subjectAcute Disease
dc.subjectRadiotherapy Dosage
dc.subjectLogistic Models
dc.subjectProbability
dc.subjectModels, Theoretical
dc.subjectFemale
dc.subjectMale
dc.subjectMachine Learning
dc.subjectClinical Decision-Making
dc.titleNormal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.
dc.typeJournal Article
dcterms.dateAccepted2016-05-12
rioxxterms.versionofrecord10.1016/j.radonc.2016.05.015
rioxxterms.licenseref.startdate2016-07
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
pubs.issue1
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology/Targeted Therapy
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Clinical Academic Radiotherapy (Horwich)
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Targeted Therapy
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology/Targeted Therapy
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Clinical Academic Radiotherapy (Horwich)
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Radiotherapy Physics Modelling
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Targeted Therapy
pubs.publication-statusPublished
pubs.volume120
pubs.embargo.termsNo embargo
icr.researchteamClinical Academic Radiotherapy (Horwich)en_US
icr.researchteamRadiotherapy Physics Modellingen_US
icr.researchteamTargeted Therapyen_US
dc.contributor.icrauthorDean, Jamieen
dc.contributor.icrauthorHarrington, Kevinen
dc.contributor.icrauthorGulliford, Sarahen


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