001     130210
005     20240228143416.0
024 7 _ |a 10.1186/s12883-016-0576-5
|2 doi
024 7 _ |a pmid:27094741
|2 pmid
024 7 _ |a pmc:PMC4837540
|2 pmc
037 _ _ |a DKFZ-2017-05290
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Mundiyanapurath, Sibu
|b 0
245 _ _ |a Time-dependent parameter of perfusion imaging as independent predictor of clinical outcome in symptomatic carotid artery stenosis.
260 _ _ |a London
|c 2016
|b BioMed Central
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1525684388_7590
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Carotid artery stenosis is a frequent cause of ischemic stroke. While any degree of stenosis can cause embolic stroke, a higher degree of stenosis can also cause hemodynamic infarction. The hemodynamic effect of a stenosis can be assessed via perfusion weighted MRI (PWI). Our aim was to investigate the ability of PWI-derived parameters such as TTP (time-to-peak) and T(max) (time to the peak of the residue curve) to predict outcome in patients with unilateral acute symptomatic internal carotid artery (sICA) stenosis.Patients with unilateral acute sICA stenosis (≥50% according to NASCET), without intracranial stenosis or occlusion, who underwent PWI, were included. Clinical characteristics, volume of restricted diffusion, volume of prolonged TTP and T(max) were retrospectively analyzed and correlated with outcome represented by the modified Rankin Scale (mRS) score at discharge. TTP and T(max) volumes were dichotomized using a ROC curve analysis. Multivariate analysis was performed to determine which PWI-parameter was an independent predictor of outcome.Thirty-two patients were included. Degree of stenosis, volume of visually assessed TTP and volume of TTP ≥2 s did not distinguish patients with favorable (mRS 0-2) and unfavorable (mRS 3-6) outcome. In contrast, patients with unfavorable outcome had higher volumes of TTP ≥4 s (9.12 vs. 0.87 ml; p = 0.043), TTP ≥6 s (6.70 vs. 0.20 ml; p = 0.017), T(max) ≥4 s (25.27 vs. 0.00 ml; p = 0.043), T(max) ≥6 s (9.21 vs. 0.00 ml; p = 0.017), T(max) ≥8 s (6.86 vs. 0.00 ml; p = 0.011) and T(max) ≥10s (5.94 vs. 0.00 ml; p = 0.025) in univariate analysis. Multivariate logistic regression showed that NIHSS score on admission (Odds Ratio (OR) 0.466, confidence interval (CI) [0.224;0.971], p = 0.041), T(max) ≥8 s (OR 0.025, CI [0.001;0.898] p = 0.043) and TTP ≥6 s (OR 0.025, CI [0.001;0.898] p = 0.043) were independent predictors of clinical outcome.As they stood out in multivariate regression and are objective and reproducible parameters, PWI-derived volumes of T(max) ≥8 s and TTP ≥6 s might be superior to degree of stenosis and visually assessed TTP maps in predicting short term patient outcome. Future studies should assess if perfusion weighted imaging might guide the selection of patients for recanalization procedures.
536 _ _ |a 315 - Imaging and radiooncology (POF3-315)
|0 G:(DE-HGF)POF3-315
|c POF3-315
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
700 1 _ |a Ringleb, Peter Arthur
|b 1
700 1 _ |a Diatschuk, Sascha
|b 2
700 1 _ |a Eidel, Oliver
|b 3
700 1 _ |a Burth, Sina
|b 4
700 1 _ |a Floca, Ralf Omar
|0 P:(DE-He78)f0ab09cfecf353f363bab4cc983de95d
|b 5
|u dkfz
700 1 _ |a Möhlenbruch, Markus
|b 6
700 1 _ |a Wick, Wolfgang
|0 P:(DE-He78)92e9783ca7025f36ce14e12cd348d2ee
|b 7
|u dkfz
700 1 _ |a Bendszus, Martin
|b 8
700 1 _ |a Radbruch, Alexander
|0 P:(DE-He78)77588f5b9413339755a66e739d316c7d
|b 9
|e Last author
|u dkfz
773 _ _ |a 10.1186/s12883-016-0576-5
|g Vol. 16, no. 1, p. 50
|0 PERI:(DE-600)2041347-6
|n 1
|p 50
|t BMC neurology
|v 16
|y 2016
|x 1471-2377
909 C O |o oai:inrepo02.dkfz.de:130210
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)f0ab09cfecf353f363bab4cc983de95d
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 7
|6 P:(DE-He78)92e9783ca7025f36ce14e12cd348d2ee
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 9
|6 P:(DE-He78)77588f5b9413339755a66e739d316c7d
913 1 _ |a DE-HGF
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-315
|2 G:(DE-HGF)POF3-300
|v Imaging and radiooncology
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Gesundheit
914 1 _ |y 2016
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b BMC NEUROL : 2015
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
920 1 _ |0 I:(DE-He78)E071-20160331
|k E071
|l Softwareentwicklung für Integrierte Diagnostik und Therapie(SIDT)
|x 0
920 1 _ |0 I:(DE-He78)G370-20160331
|k G370
|l KKE Neuroonkologie
|x 1
920 1 _ |0 I:(DE-He78)E012-20160331
|k E012
|l Neuroonkologische Bildgebung
|x 2
920 1 _ |0 I:(DE-He78)L101-20160331
|k L101
|l DKTK Heidelberg
|x 3
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)E071-20160331
980 _ _ |a I:(DE-He78)G370-20160331
980 _ _ |a I:(DE-He78)E012-20160331
980 _ _ |a I:(DE-He78)L101-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21