dc.contributor.author | Dean, JA | |
dc.contributor.author | Wong, KH | |
dc.contributor.author | Welsh, LC | |
dc.contributor.author | Jones, A-B | |
dc.contributor.author | Schick, U | |
dc.contributor.author | Newbold, KL | |
dc.contributor.author | Bhide, SA | |
dc.contributor.author | Harrington, KJ | |
dc.contributor.author | Nutting, CM | |
dc.contributor.author | Gulliford, SL | |
dc.date.accessioned | 2016-08-26T15:19:06Z | |
dc.date.issued | 2016-07-01 | |
dc.identifier.citation | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016, 120 (1), pp. 21 - 27 | |
dc.identifier.issn | 0167-8140 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/77 | |
dc.identifier.eissn | 1879-0887 | |
dc.identifier.doi | 10.1016/j.radonc.2016.05.015 | |
dc.description.abstract | BACKGROUND 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.format | Print-Electronic | |
dc.format.extent | 21 - 27 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | ELSEVIER IRELAND LTD | |
dc.subject | Humans | |
dc.subject | Head and Neck Neoplasms | |
dc.subject | Stomatitis | |
dc.subject | Radiation Injuries | |
dc.subject | Acute Disease | |
dc.subject | Radiotherapy Dosage | |
dc.subject | Logistic Models | |
dc.subject | Probability | |
dc.subject | Models, Theoretical | |
dc.subject | Female | |
dc.subject | Male | |
dc.subject | Machine Learning | |
dc.subject | Clinical Decision-Making | |
dc.title | 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. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2016-05-12 | |
rioxxterms.versionofrecord | 10.1016/j.radonc.2016.05.015 | |
rioxxterms.licenseref.startdate | 2016-07 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology | |
pubs.issue | 1 | |
pubs.notes | No 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-status | Published | |
pubs.volume | 120 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Clinical Academic Radiotherapy (Horwich) | |
icr.researchteam | Radiotherapy Physics Modelling | |
icr.researchteam | Targeted Therapy | |
dc.contributor.icrauthor | Dean, Jamie | |
dc.contributor.icrauthor | Harrington, Kevin | |