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dc.contributor.authorCoello, Cen
dc.contributor.authorFisk, Marieen
dc.contributor.authorMohan, Den
dc.contributor.authorWilson, FJen
dc.contributor.authorBrown, APen
dc.contributor.authorPolkey, MIen
dc.contributor.authorWilkinson, Ianen
dc.contributor.authorTal-Singer, Ren
dc.contributor.authorMurphy, PSen
dc.contributor.authorCheriyan, Josephen
dc.contributor.authorGunn, RNen
dc.date.accessioned2017-08-07T11:05:53Z
dc.date.available2017-08-07T11:05:53Z
dc.date.issued2017-05-25en
dc.identifier.issn2191-219X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/265951
dc.description.abstract$\textbf{Background:}$ An inflammatory reaction in the airways and lung parenchyma, comprised mainly of neutrophils and alveolar macrophages, is present in some patients with chronic obstructive pulmonary disease (COPD). Thoracic fluorodeoxyglucose $^{18}$F-FDG) positron emission tomography (PET) has been proposed as a promising imaging biomarker to assess this inflammation. We sought to introduce a fully quantitative analysis method and compare this with previously published studies based on the Patlak approach using a dataset comprising $^{18}$F-FDG PET scans from COPD subjects with elevated circulating inflammatory markers (fibrinogen) and matched healthy volunteers (HV). Dynamic $^{18}$F-FDG PET scans were obtained for high-fibrinogen (>2.8 g/l) COPD subjects (N = 10) and never smoking HV (N = 10). Lungs were segmented using co-registered computed tomography images and subregions (upper, middle and lower) were semi-automatically defined. A quantitative analysis approach was developed, which corrects for the presence of air and blood in the lung (qABL method), enabling direct estimation of the metabolic rate of FDG in lung tissue. A normalised Patlak analysis approach was also performed to enable comparison with previously published results. Effect sizes (Hedge's g) were used to compare HV and COPD groups. $\textbf{Results:}$ The qABL method detected no difference (Hedge's g = 0.15 [-0.76 1.04]) in the tissue metabolic rate of FDG in the whole lung between HV (μ = 6.0 ± 1.9 × 10$^{-3}$ ml cm$^{-3}$ min$^{-1}$) and COPD (μ = 5.7 ± 1.7 × 10$^{-3}$ ml cm$^{-3}$ min$^{-1}$). However, analysis with the normalised Patlak approach detected a significant difference (Hedge's g = -1.59 [-2.57 -0.48]) in whole lung between HV (μ = 2.9 ± 0.5 × 10$^{-3}$ ml cm$^{-3}$ min$^{-1}$) and COPD (μ = 3.9 ± 0.7 × 10$^{-3}$ ml cm$^{-3}$ min$^{-1}$). The normalised Patlak endpoint was shown to be a composite measure influenced by air volume, blood volume and actual uptake of $^{18}$F-FDG in lung tissue. $\textbf{Conclusions:}$ We have introduced a quantitative analysis method that provides a direct estimate of the metabolic rate of FDG in lung tissue. This work provides further understanding of the underlying origin of the $^{18}$F-FDG signal in the lung in disease groups and helps interpreting changes following standard or novel therapies.
dc.description.sponsorshipJC and IBW acknowledge the funding support from the Cambridge Comprehensive Biomedical Research Centre. MIP’s contribution to this work was funded by the NIHR Respiratory Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust and Imperial College who part fund his salary. EVOLUTION and EVOLVE studies were funded by Innovate UK under the ERICA consortium grant with in kind contributions from GSK.
dc.language.isoenen
dc.publisherSpringer
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectPETen
dc.subject18F-FDGen
dc.subjectlung inflammationen
dc.subjectmodellingen
dc.subjectCOPDen
dc.titleQuantitative analysis of dynamic 18F-FDG PET/CT for measurement of lung inflammationen
dc.typeArticle
prism.number47en
prism.publicationDate2017en
prism.publicationNameEJNMMI Researchen
prism.volume7en
dc.identifier.doi10.17863/CAM.10280
dcterms.dateAccepted2017-05-09en
rioxxterms.versionofrecord10.1186/s13550-017-0291-2en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-05-25en
dc.contributor.orcidFisk, Marie [0000-0002-1292-7642]
dc.contributor.orcidWilkinson, Ian [0000-0001-6598-9399]
dc.contributor.orcidCheriyan, Joseph [0000-0001-6921-1592]
dc.identifier.eissn2191-219X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idTechnology Strategy Board (101024 TP 9157-61188)
pubs.funder-project-idBritish Heart Foundation (FS/07/001/21990)
pubs.funder-project-idBritish Heart Foundation (FS/12/8/29377)
pubs.funder-project-idDepartment of Health (via National Institute for Health Research (NIHR)) (unknown)
pubs.funder-project-idNational Institute for Health Research (NIHR) (via Cambridgeshire and Peterborough Clinical Commissioning Group (CCG)) (unknown)
pubs.funder-project-idNIHR West Anglia Comprehensive Local Research Network (CLRN) (via Cambridge University Hospitals NHS Foundation Trust (CUH)) (CLRN ERICA)
pubs.funder-project-idCambridge University Hospitals NHS Foundation Trust (CUH) (146281)
pubs.funder-project-idBritish Heart Foundation (FS/12/33/29561)
cam.issuedOnline2017-05-25en


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International