Show simple item record

dc.contributor.authorDean, J
dc.contributor.authorWong, K
dc.contributor.authorGay, H
dc.contributor.authorWelsh, L
dc.contributor.authorJones, A-B
dc.contributor.authorSchick, U
dc.contributor.authorOh, JH
dc.contributor.authorApte, A
dc.contributor.authorNewbold, K
dc.contributor.authorBhide, S
dc.contributor.authorHarrington, K
dc.contributor.authorDeasy, J
dc.contributor.authorNutting, C
dc.contributor.authorGulliford, S
dc.date.accessioned2018-04-23T14:38:24Z
dc.date.issued2018-01-01
dc.identifier.citationClinical and translational radiation oncology, 2018, 8 pp. 27 - 39
dc.identifier.issn2405-6308
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/1651
dc.identifier.eissn2405-6308
dc.identifier.doi10.1016/j.ctro.2017.11.009
dc.description.abstractSevere acute dysphagia commonly results from head and neck radiotherapy (RT). A model enabling prediction of severity of acute dysphagia for individual patients could guide clinical decision-making. Statistical associations between RT dose distributions and dysphagia could inform RT planning protocols aiming to reduce the incidence of severe dysphagia. We aimed to establish such a model and associations incorporating spatial dose metrics. Models of severe acute dysphagia were developed using pharyngeal mucosa (PM) RT dose (dose-volume and spatial dose metrics) and clinical data. Penalized logistic regression (PLR), support vector classification and random forest classification (RFC) models were generated and internally (173 patients) and externally (90 patients) validated. These were compared using area under the receiver operating characteristic curve (AUC) to assess performance. Associations between treatment features and dysphagia were explored using RFC models. The PLR model using dose-volume metrics (PLRstandard) performed as well as the more complex models and had very good discrimination (AUC = 0.82) on external validation. The features with the highest RFC importance values were the volume, length and circumference of PM receiving 1 Gy/fraction and higher. The volumes of PM receiving 1 Gy/fraction or higher should be minimized to reduce the incidence of severe acute dysphagia.
dc.formatPrint-Electronic
dc.format.extent27 - 39
dc.languageeng
dc.language.isoeng
dc.publisherELSEVIER IRELAND LTD
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleIncorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy.
dc.typeJournal Article
rioxxterms.versionofrecord10.1016/j.ctro.2017.11.009
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
rioxxterms.licenseref.startdate2018-01
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfClinical and translational radiation oncology
pubs.notesNot known
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.volume8
pubs.embargo.termsNot known
icr.researchteamClinical Academic Radiotherapy (Horwich)
icr.researchteamRadiotherapy Physics Modelling
icr.researchteamTargeted Therapy
dc.contributor.icrauthorDean, Jamie
dc.contributor.icrauthorHarrington, Kevin


Files in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record

https://creativecommons.org/licenses/by/4.0
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0