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dc.contributor.authorMcHugh, DJ
dc.contributor.authorPorta, N
dc.contributor.authorLittle, RA
dc.contributor.authorCheung, S
dc.contributor.authorWatson, Y
dc.contributor.authorParker, GJM
dc.contributor.authorJayson, GC
dc.contributor.authorO'Connor, JPB
dc.date.accessioned2021-01-15T10:26:00Z
dc.date.issued2021-01-11
dc.identifier.citationCancers, 2021, 13 (2)
dc.identifier.issn2072-6694
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4286
dc.identifier.eissn2072-6694
dc.identifier.doi10.3390/cancers13020240
dc.description.abstractImaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box-Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable; volume ICC > 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.
dc.formatElectronic
dc.languageeng
dc.language.isoeng
dc.publisherMDPI
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleImage Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases.
dc.typeJournal Article
dcterms.dateAccepted2021-01-05
rioxxterms.versionofrecord10.3390/cancers13020240
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2021-01-11
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfCancers
pubs.issue2
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/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
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/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.publication-statusPublished
pubs.volume13
pubs.embargo.termsNot known
icr.researchteamClinical Trials & Statistics Unit
dc.contributor.icrauthorPorta, Nuria
dc.contributor.icrauthorO'Connor, James Patrick


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