dc.contributor.author | Dean, JA | |
dc.contributor.author | Welsh, LC | |
dc.contributor.author | McQuaid, D | |
dc.contributor.author | Wong, KH | |
dc.contributor.author | Aleksic, A | |
dc.contributor.author | Dunne, E | |
dc.contributor.author | Islam, MR | |
dc.contributor.author | Patel, A | |
dc.contributor.author | Patel, P | |
dc.contributor.author | Petkar, I | |
dc.contributor.author | Phillips, I | |
dc.contributor.author | Sham, J | |
dc.contributor.author | Newbold, KL | |
dc.contributor.author | Bhide, SA | |
dc.contributor.author | Harrington, KJ | |
dc.contributor.author | Gulliford, SL | |
dc.contributor.author | Nutting, CM | |
dc.date.accessioned | 2016-08-26T15:22:20Z | |
dc.date.issued | 2016-04-01 | |
dc.identifier.citation | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016, 119 (1), pp. 166 - 171 | |
dc.identifier.issn | 0167-8140 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/79 | |
dc.identifier.eissn | 1879-0887 | |
dc.identifier.doi | 10.1016/j.radonc.2016.02.022 | |
dc.description.abstract | BACKGROUND AND PURPOSE: Current oral mucositis normal tissue complication probability models, based on the dose distribution to the oral cavity volume, have suboptimal predictive power. Improving the delineation of the oral mucosa is likely to improve these models, but is resource intensive. We developed and evaluated fully-automated atlas-based segmentation (ABS) of a novel delineation technique for the oral mucosal surfaces. MATERIAL AND METHODS: An atlas of mucosal surface contours (MSC) consisting of 46 patients was developed. It was applied to an independent test cohort of 10 patients for whom manual segmentation of MSC structures, by three different clinicians, and conventional outlining of oral cavity contours (OCC), by an additional clinician, were also performed. Geometric comparisons were made using the dice similarity coefficient (DSC), validation index (VI) and Hausdorff distance (HD). Dosimetric comparisons were carried out using dose-volume histograms. RESULTS: The median difference, in the DSC and HD, between automated-manual comparisons and manual-manual comparisons were small and non-significant (-0.024; p=0.33 and -0.5; p=0.88, respectively). The median VI was 0.086. The maximum normalised volume difference between automated and manual MSC structures across all of the dose levels, averaged over the test cohort, was 8%. This difference reached approximately 28% when comparing automated MSC and OCC structures. CONCLUSIONS: Fully-automated ABS of MSC is suitable for use in radiotherapy dose-response modelling. | |
dc.format | Print-Electronic | |
dc.format.extent | 166 - 171 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | ELSEVIER IRELAND LTD | |
dc.subject | Mouth Mucosa | |
dc.subject | Humans | |
dc.subject | Head and Neck Neoplasms | |
dc.subject | Radiotherapy Dosage | |
dc.subject | Radiometry | |
dc.subject | Dose-Response Relationship, Radiation | |
dc.subject | Atlases as Topic | |
dc.subject | Organs at Risk | |
dc.title | Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2016-02-09 | |
rioxxterms.versionofrecord | 10.1016/j.radonc.2016.02.022 | |
rioxxterms.licenseref.startdate | 2016-04 | |
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 | 119 | |
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 | Patel, Priyanka | |
dc.contributor.icrauthor | Harrington, Kevin | |