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dc.contributor.authorTar, PD
dc.contributor.authorThacker, NA
dc.contributor.authorBabur, M
dc.contributor.authorWatson, Y
dc.contributor.authorCheung, S
dc.contributor.authorLittle, RA
dc.contributor.authorGieling, RG
dc.contributor.authorWilliams, KJ
dc.contributor.authorO'Connor, JPB
dc.date.accessioned2020-08-12T14:46:31Z
dc.date.issued2018-08-01
dc.identifier.citationBioinformatics (Oxford, England), 2018, 34 (15), pp. 2625 - 2633
dc.identifier.issn1367-4803
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3942
dc.identifier.eissn1367-4811
dc.identifier.doi10.1093/bioinformatics/bty115
dc.description.abstractMOTIVATION: Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both development and also in response to treatment. The large variations observed in control group, tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, linear Poisson modeling (LPM) evaluates changes in apparent diffusion co-efficient between baseline and 72 h after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes is compared to those attainable using a conventional t-test analysis on basic apparent diffusion co-efficient distribution parameters. RESULTS: When LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4-fold (i.e. equivalent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalized medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes. AVAILABILITY AND IMPLEMENTATION: TINA Vision open source software is available from www.tina-vision.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
dc.formatPrint
dc.format.extent2625 - 2633
dc.languageeng
dc.language.isoeng
dc.publisherOXFORD UNIV PRESS
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectCell Line, Tumor
dc.subjectHCT116 Cells
dc.subjectAnimals
dc.subjectHumans
dc.subjectMice
dc.subjectNeoplasms
dc.subjectColorectal Neoplasms
dc.subjectMagnetic Resonance Imaging
dc.subjectTreatment Outcome
dc.subjectModels, Statistical
dc.subjectLinear Models
dc.subjectSample Size
dc.subjectXenograft Model Antitumor Assays
dc.subjectComputational Biology
dc.subjectSoftware
dc.subjectFemale
dc.titleA new method for the high-precision assessment of tumor changes in response to treatment.
dc.typeJournal Article
dcterms.dateAccepted2018-03-12
rioxxterms.versionofrecord10.1093/bioinformatics/bty115
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2018-08
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBioinformatics (Oxford, England)
pubs.issue15
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.volume34
pubs.embargo.termsNot known
icr.researchteamQuantitative Biomedical Imaging
dc.contributor.icrauthorO'Connor, James Patrick


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