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dc.contributor.authorHumby, FCen_US
dc.contributor.authorAl Balushi, Fen_US
dc.contributor.authorLliso, Gen_US
dc.contributor.authorCauli, Aen_US
dc.contributor.authorPitzalis, Cen_US
dc.date.accessioned2018-03-13T13:06:57Z
dc.date.available2017-03-27en_US
dc.date.issued2017en_US
dc.date.submitted2018-03-13T13:00:41.134Z
dc.identifier.issn2296-858Xen_US
dc.identifier.other10.3389/fmed.2017.00041
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/34988
dc.description.abstractAlthough great progress has been made in the past decade toward understanding the pathogenesis of rheumatoid arthritis (RA), clinicians remain some distance from a goal of personalized health care. The capacity to diagnose RA early, predict prognosis, and moreover predict response to biologic therapies has been a research focus for many years. How currently available clinical prediction models can facilitate such goals is reviewed in this article. In addition, the role of current imaging techniques in this regard is also discussed. Finally, the authors review the current literature regarding synovial biomarkers and consider whether integration of synovial pathobiology into clinical prediction algorithms may enhance their predictive value.en_US
dc.format.extent41 - ?en_US
dc.languageengen_US
dc.language.isoenen_US
dc.relation.ispartofFront Med (Lausanne)en_US
dc.rightsCreative Commons Attribution License
dc.subjectimagingen_US
dc.subjectpathologyen_US
dc.subjectpersonalized medicineen_US
dc.subjectrheumatoid arthritisen_US
dc.subjectsynovial membraneen_US
dc.titleCan Synovial Pathobiology Integrate with Current Clinical and Imaging Prediction Models to Achieve Personalized Health Care in Rheumatoid Arthritis?en_US
dc.typeArticle
dc.rights.holder2017 Humby, Al Balushi, Lliso, Cauli and Pitzalis.
dc.identifier.doi10.3389/fmed.2017.00041en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/28516086en_US
pubs.notesNo embargoen_US
pubs.publication-statusPublished onlineen_US
pubs.volume4en_US
dcterms.dateAccepted2017-03-27en_US


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