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dc.contributor.authorStarck, Lea
dc.contributor.authorZaccagna, Fulvio
dc.contributor.authorPasternak, Ofer
dc.contributor.authorGallagher, Ferdia
dc.contributor.authorGrüner, Renate
dc.contributor.authorRiemer, Frank
dc.date.accessioned2022-01-05T16:29:17Z
dc.date.available2022-01-05T16:29:17Z
dc.date.issued2021-12-17
dc.identifier.issn2075-4418
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332046
dc.description.abstractDiffusion MRI is a useful tool to investigate the microstructure of brain tumors. However, the presence of fast diffusing isotropic signals originating from non-restricted edematous fluids, within and surrounding tumors, may obscure estimation of the underlying tissue characteristics, complicating the radiological interpretation and quantitative evaluation of diffusion MRI. A multi-shell regularized free water (FW) elimination model was therefore applied to separate free water from tissue-related diffusion components from the diffusion MRI of 26 treatment-naïve glioma patients. We then investigated the diagnostic value of the derived measures of FW maps as well as FW-corrected tensor-derived maps of fractional anisotropy (FA). Presumed necrotic tumor regions display greater mean and variance of FW content than other parts of the tumor. On average, the area under the receiver operating characteristic (ROC) for the classification of necrotic and enhancing tumor volumes increased by 5% in corrected data compared to non-corrected data. FW elimination shifts the FA distribution in non-enhancing tumor parts toward higher values and significantly increases its entropy (p ≤ 0.003), whereas skewness is decreased (p ≤ 0.004). Kurtosis is significantly decreased (p < 0.001) in high-grade tumors. In conclusion, eliminating FW contributions improved quantitative estimations of FA, which helps to disentangle the cancer heterogeneity.
dc.languageen
dc.publisherMDPI AG
dc.subjectglioma
dc.subjectfree water
dc.subjecttumor characterization
dc.titleEffects of Multi-Shell Free Water Correction on Glioma Characterization.
dc.typeArticle
dc.date.updated2022-01-05T16:29:16Z
prism.issueIdentifier12
prism.publicationNameDiagnostics (Basel)
prism.volume11
dc.identifier.doi10.17863/CAM.79493
dcterms.dateAccepted2021-12-13
rioxxterms.versionofrecord10.3390/diagnostics11122385
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidStarck, Lea [0000-0003-4996-3038]
dc.contributor.orcidZaccagna, Fulvio [0000-0001-6838-9532]
dc.contributor.orcidGallagher, Ferdia [0000-0003-4784-5230]
dc.contributor.orcidRiemer, Frank [0000-0002-3805-5221]
dc.identifier.eissn2075-4418
pubs.funder-project-idTrond Mohn Foundation (BFS2018TMT05)
pubs.funder-project-idCancer Research UK (C19212/A16628)
pubs.funder-project-idEngineering and Physical Sciences Research Council (C197/A16465)
cam.issuedOnline2021-12-17


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