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Diagnostic Ability of Macular Nerve Fiber Layer Thickness Using New Segmentation Software in Glaucoma Suspects

dc.contributor.authorMartínez De La Casa Fernández-Borrella, José María
dc.contributor.authorCifuentes Canorea, Pilar
dc.contributor.authorBerrozpe Villabona, Clara
dc.contributor.authorSastre Ibáñez, Marina
dc.contributor.authorPolo Llorens, Vicente
dc.contributor.authorMoreno Montañes, Javier
dc.contributor.authorGarcía Feijoo, Julián
dc.date.accessioned2023-06-19T15:07:10Z
dc.date.available2023-06-19T15:07:10Z
dc.date.copyrightEn abierto en la web del editor © ARVO Submitted: August 19, 2014 / Accepted: November 4, 2014
dc.date.issued2014-12
dc.description.abstractPurpose. To assess the capacity of internal retinal layer thickness measurements made at the macula using new spectral-domain optical coherence tomography (OCT) software to distinguish between healthy subjects and those with suspected glaucoma. The diagnostic performance of such measurements also was compared with that of conventional peripapillary retinal nerve fiber layer (RNFL) thickness measurements. Methods. The study included 38 subjects with suspected glaucoma and 38 age-matched healthy subjects. In one randomly selected eye of each participant, thickness measurements at the level of the macula were made of the nerve fiber layer (mRNFL), the ganglion cell layer (GCL), and the ganglion cell complex (GCC; GCL + internal plexiform layer) through automated OCT segmentation. Peripapillary RNFL thickness (pRNFL) also was determined using the conventional scan. Results. As the only variable showing intergroup variation, mRNFL in the glaucoma suspects was significantly thinner in the quadrants inner inferior (P = 0.003), inner temporal (P = 0.010), and outer inferior (P = 0.017). The variable best able to discriminate between the two groups was inner inferior mRNFL thickness, as indicated by an area below the receiver operating characteristic (ROC) curve of 0.742. Conclusions. Macular RNFL thickness measurements showed an improved diagnostic capacity over the other variables examined to distinguish between healthy subjects and glaucoma suspects.en
dc.description.departmentUnidad Docente de Inmunología, Oftalmología y ORL
dc.description.facultyFac. de Óptica y Optometría
dc.description.refereedTRUE
dc.description.sponsorshipInstituto de Salud Carlos III
dc.description.sponsorshipUniversidad Complutense de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/40997
dc.identifier.doi10. 1167/iovs.14-15501
dc.identifier.issn0146-0404
dc.identifier.officialurlhttp://dx.doi.org/10. 1167/iovs.14-15501
dc.identifier.relatedurlhttp://iovs.arvojournals.org/article.aspx?articleid=2212697&resultClick=1
dc.identifier.urihttps://hdl.handle.net/20.500.14352/35374
dc.issue.number12
dc.journal.titleInvestigative Ophthalmology & Visual Science
dc.language.isoeng
dc.page.final8348
dc.page.initial8343
dc.publisherThe Association for Research in Vision and Ophthalmology, Inc.
dc.relation.projectIDProyecto RD07/0062: Patología ocular del envejecimiento, calidad visual y calidad de vida
dc.relation.projectID920415-GR58/08
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.cdu617.7-007.681
dc.subject.cdu617.749-073
dc.subject.cdu617.73
dc.subject.keywordGlaucoma
dc.subject.keywordOptical coherence tomography
dc.subject.keywordMacula
dc.subject.keywordRetinal nerve fiber layer
dc.subject.keywordGanglion cell
dc.subject.ucmDiagnóstico por imagen y medicina nuclear
dc.subject.ucmOftalmología
dc.subject.unesco3204.01 Medicina Nuclear
dc.subject.unesco3201.09 Oftalmología
dc.titleDiagnostic Ability of Macular Nerve Fiber Layer Thickness Using New Segmentation Software in Glaucoma Suspectsen
dc.typejournal article
dc.volume.number55
dspace.entity.typePublication
relation.isAuthorOfPublication273a99c3-2c9f-4dd0-8939-b7ff3593124c
relation.isAuthorOfPublication558b8023-6d72-4dff-9f99-2e60f6f31843
relation.isAuthorOfPublication.latestForDiscovery558b8023-6d72-4dff-9f99-2e60f6f31843

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