Show simple item record

dc.contributor.authorFeatherstone, AK
dc.contributor.authorO'Connor, JPB
dc.contributor.authorLittle, RA
dc.contributor.authorWatson, Y
dc.contributor.authorCheung, S
dc.contributor.authorBabur, M
dc.contributor.authorWilliams, KJ
dc.contributor.authorMatthews, JC
dc.contributor.authorParker, GJM
dc.date.accessioned2020-08-12T14:47:54Z
dc.date.issued2018-04
dc.identifier.citationMagnetic resonance in medicine, 2018, 79 (4), pp. 2236 - 2245
dc.identifier.issn0740-3194
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3944
dc.identifier.eissn1522-2594en_US
dc.identifier.doi10.1002/mrm.26860en_US
dc.description.abstractPurpose Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data.Methods DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach.Results The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75).Conclusion The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.formatPrint-Electronic
dc.format.extent2236 - 2245
dc.languageeng
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAnimals
dc.subjectHumans
dc.subjectMice
dc.subjectNeoplasms
dc.subjectGlioblastoma
dc.subjectCarcinoma, Non-Small-Cell Lung
dc.subjectLung Neoplasms
dc.subjectOxygen
dc.subjectImage Interpretation, Computer-Assisted
dc.subjectMagnetic Resonance Imaging
dc.subjectArea Under Curve
dc.subjectCluster Analysis
dc.subjectNormal Distribution
dc.subjectReproducibility of Results
dc.subjectNeoplasm Transplantation
dc.subjectPerfusion
dc.subjectAlgorithms
dc.subjectPrincipal Component Analysis
dc.subjectImage Processing, Computer-Assisted
dc.subjectSoftware
dc.subjectTumor Microenvironment
dc.subjectHypoxia
dc.subjectTumor Hypoxia
dc.titleData-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI.
dc.typeJournal Article
dcterms.dateAccepted2017-07-13
rioxxterms.versionofrecord10.1002/mrm.26860
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2018-04en_US
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfMagnetic resonance in medicine
pubs.issue4
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/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Quantitative Biomedical Imaging
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/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Quantitative Biomedical Imaging
pubs.publication-statusPublished
pubs.volume79en_US
pubs.embargo.termsNot known
icr.researchteamQuantitative Biomedical Imagingen_US
dc.contributor.icrauthorO'Connor, James Patricken


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

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