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dc.contributor.authorHARDIMAN, ORLAen
dc.date.accessioned2017-02-20T14:21:54Z
dc.date.available2017-02-20T14:21:54Z
dc.date.issued2016en
dc.date.submitted2016en
dc.identifier.citationSchuster C, Hardiman O, Bede P, Development of an automated MRI-based diagnostic protocol for amyotrophic lateral sclerosis using disease-specific pathognomonic features: A quantitative disease-state classification study, PLoS ONE, 11, 12, 2016, 016733-en
dc.identifier.otherYen
dc.identifier.urihttp://hdl.handle.net/2262/79436
dc.description.abstractBackground Despite significant advances in quantitative neuroimaging, the diagnosis of ALS remains clinical and MRI-based biomarkers are not currently used to aid the diagnosis. The objective of this study is to develop a robust, disease-specific, multimodal classification protocol and validate its diagnostic accuracy in independent, early-stage and follow-up data sets. Methods 147 participants (81 ALS patients and 66 healthy controls) were divided into a training sample and a validation sample. Patients in the validation sample underwent follow-up imaging longitudinally. After removing age-related variability, indices of grey and white matter integrity in ALS-specific pathognomonic brain regions were included in a cross-validated binary logistic regression model to determine the probability of individual scans indicating ALS. The following anatomical regions were assessed for diagnostic classification: average grey matter density of the left and right precentral gyrus, the average fractional anisotropy and radial diffusivity of the left and right superior corona radiata, inferior corona radiata, internal capsule, mesencephalic crus of the cerebral peduncles, pontine segment of the corticospinal tract, and the average diffusivity values of the genu, corpus and splenium of the corpus callosum. Results Using a 50% probability cut-off value of suffering from ALS, the model was able to discriminate ALS patients and HC with good sensitivity (80.0%) and moderate accuracy (70.0%) in the training sample and superior sensitivity (85.7%) and accuracy (78.4%) in the independent validation sample. Conclusions This diagnostic classification study endeavours to advance ALS biomarker research towards pragmatic clinical applications by providing an approach of automated individual-data interpretation based on group-level observations.en
dc.description.sponsorshipThis work was supported by the Irish Institute of Clinical Neuroscience (IICN)—Novartis Ireland Research Grant, The Iris O'Brien Foundation, The Perrigo Clinician-Scientist Research Fellowship, the Health Research Board and the Research Motor Neuron (RMN-Ireland) foundation. Professor Hardiman’s research group has also received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° [259867] (EUROMOTOR), the EU-Joint Programme for Neurodegeneration (JPND) SOPHIA project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.format.extent016733en
dc.relation.ispartofseriesPLoS ONEen
dc.relation.ispartofseries11en
dc.relation.ispartofseries12en
dc.rightsYen
dc.subjectALS biomarkeren
dc.subject.lcshALS biomarkeren
dc.titleDevelopment of an automated MRI-based diagnostic protocol for amyotrophic lateral sclerosis using disease-specific pathognomonic features: A quantitative disease-state classification studyen
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/hardimaoen
dc.identifier.rssinternalid150244en
dc.identifier.doihttp://dx.doi.org/10.1371/journal.pone.0167331en
dc.rights.ecaccessrightsopenAccess
dc.identifier.rssurihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84999862829&doi=10.1371%2fjournal.pone.0167331&partnerID=40&md5=f9d37cbf42a388a0a147ae7df44115e0en
dc.identifier.orcid_id0000-0003-2610-1291en


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