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Scheldeman L, Wouters A, Dupont P, Christensen S, Boutitie F, Cheng B, Ebinger M, Endres M, Fiebach JB, Gerloff C, Muir KW, Nighoghossian N, Pedraza S, Simonsen CZ, Thijs V, Thomalla G, Lemmens R. Diffusion-Weighted Imaging and Fluid-Attenuated Inversion Recovery Quantification to Predict Diffusion-Weighted Imaging-Fluid-Attenuated Inversion Recovery Mismatch Status in Ischemic Stroke With Unknown Onset. Stroke 2022; 53:1665-1673. [PMID: 35105179 DOI: 10.1161/strokeaha.121.036871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Visual rating of diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch can be challenging. We evaluated quantification of DWI and FLAIR to predict DWI-FLAIR mismatch status in ischemic stroke. METHODS In screened patients from the WAKE-UP trial (Efficacy and Safety of Magnetic Resonance Imaging-Based Thrombolysis in Wake-Up Stroke), we retrospectively studied relative DWI (rDWI SI) and FLAIR signal intensity (rFLAIR SI). We defined the optimal mean rFLAIR SI and interquartile range of the rDWI SI in the DWI lesion to predict DWI-FLAIR mismatch status. We investigated agreement between each quantitative parameter and the DWI-FLAIR mismatch and the association between both quantitative parameters. We evaluated the predictive value of the quantitative parameters for excellent functional outcome by logistic regression, adjusted for DWI lesion volume, treatment, age, and National Institutes of Health Stroke Scale score. RESULTS In the rFLAIR and rDWI SI analysis, 213/369 and 241/421 subjects respectively had a DWI-FLAIR mismatch. A mean rFLAIR SI cutoff of 1.09 and interquartile range rDWI SI cutoff of 0.47 were optimal to predict the DWI-FLAIR mismatch with a sensitivity and specificity of 77% (95% CI, 71%-83%) and 67% (95% CI, 59%-74%), and 76% (95% CI, 70%-81%) and 72% (95% CI, 65%-79%), respectively. For both quantitative parameters, agreement with the DWI-FLAIR mismatch was fair (73%, κ=0.44 [95% CI, 0.35-0.54] for rFLAIR and 74%, κ=0.48 [95% CI, 0.39-0.56] for rDWI). Both quantitative parameters correlated moderately (Pearson R=0.54 [95% CI, 0.46-0.61], P<0.001, n=367). The interquartile range rDWI SI (n=188), but not the mean rFLAIR SI (n=172), was an independent predictor of excellent functional outcome (odds ratio, 0.67 per 0.1 unit increase of interquartile range rDWI SI, 95% CI, 0.51-0.89, P=0.01). CONCLUSIONS Agreement between the quantitative and qualitative approach may be insufficient to advocate DWI or FLAIR quantification as alternative for visual rating.
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Affiliation(s)
- Lauranne Scheldeman
- Department of Neurology, University Hospitals Leuven, Belgium (L.S., A.W., R.L.).,Department of Neurosciences, Experimental Neurology, KU Leuven - University of Leuven, Belgium. (L.S., A.W., R.L.).,Center for Brain and Disease Research, Laboratory of Neurobiology, VIB, Leuven, Belgium (L.S., A.W., R.L.)
| | - Anke Wouters
- Department of Neurology, University Hospitals Leuven, Belgium (L.S., A.W., R.L.).,Department of Neurosciences, Experimental Neurology, KU Leuven - University of Leuven, Belgium. (L.S., A.W., R.L.).,Center for Brain and Disease Research, Laboratory of Neurobiology, VIB, Leuven, Belgium (L.S., A.W., R.L.).,Neurology, Amsterdam University Medical Centers, the Netherlands (A.W.)
| | - Patrick Dupont
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven - University of Leuven, Belgium. (P.D.).,Leuven Brain Institute, Belgium (P.D.)
| | | | - Florent Boutitie
- Hospices Civils de Lyon, Service de Biostatistique, France (F.B.).,Université Lyon 1, Villeurbanne, France (F.B.)
| | - Bastian Cheng
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Germany (B.C., C.G., G.T.)
| | - Martin Ebinger
- Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Germany. (M. Ebinger, M. Endres, J.B.F.)
| | - Matthias Endres
- Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Germany. (M. Ebinger, M. Endres, J.B.F.).,Klinik und Hochschulambulanz für Neurologie, Charité - Universitätsmedizin Berlin, Germany. (M. Endres).,Klinik für Neurologie, Medical Park Berlin Humboldtmühle, Germany (M. Ebinger).,German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres).,German Center for Neurodegenerative Diseases (DZNE), partner site Berlin (M. Endres).,ExcellenceCluster NeuroCure (M. Endres)
| | - Jochen B Fiebach
- Center for Stroke Research Berlin (CSB), Charité - Universitätsmedizin Berlin, Germany. (M. Ebinger, M. Endres, J.B.F.)
| | - Christian Gerloff
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Germany (B.C., C.G., G.T.)
| | - Keith W Muir
- Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom (K.W.M.)
| | - Norbert Nighoghossian
- Department of Stroke Medicine, Université Claude Bernard Lyon 1, CREATIS CNRS UMR 5220-INSERM U1206, INSA- Lyon (N.N.).,Hospices Civils de Lyon, France (N.N.)
| | - Salvador Pedraza
- Department of Radiology, Institut de Diagnostic per la Image (IDI), Hospital Dr Josep Trueta, Institut d'Investigació Biomedica de Girona (IDIBGI), Parc Hospitalari Marti i Julia de Salt - Edifici M2, Girona, Spain (S.P.)
| | - Claus Z Simonsen
- Department of Neurology, Aarhus University Hospital, Denmark (C.Z.S.)
| | - Vincent Thijs
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria, Australia (V.T.).,Department of Neurology, Austin Health, Heidelberg, Victoria, Australia (V.T.)
| | - Götz Thomalla
- Klinik und Poliklinik für Neurologie, Kopf- und Neurozentrum, University Medical Center Hamburg-Eppendorf, Germany (B.C., C.G., G.T.)
| | - Robin Lemmens
- Department of Neurology, University Hospitals Leuven, Belgium (L.S., A.W., R.L.).,Department of Neurosciences, Experimental Neurology, KU Leuven - University of Leuven, Belgium. (L.S., A.W., R.L.).,Center for Brain and Disease Research, Laboratory of Neurobiology, VIB, Leuven, Belgium (L.S., A.W., R.L.)
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2
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Kishi F, Nakagawa I, Park H, Kotsugi M, Myouchin K, Motoyama Y, Nakase H. Low relative diffusion weighted image signal intensity can predict good prognosis after endovascular thrombectomy in patients with acute ischemic stroke. J Neurointerv Surg 2021; 14:618-622. [PMID: 34140286 DOI: 10.1136/neurintsurg-2021-017583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 05/31/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND It is vital to identify a surrogate last-known-well time to perform proper endovascular thrombectomy in acute ischemic stroke; however, no established imaging biomarker can easily and quickly identify eligibility for endovascular thrombectomy and predict good clinical prognosis. OBJECTIVE To investigate whether low relative diffusion-weighted imaging (DWI) signal intensity can be used as a predictor of good clinical outcome after endovascular thrombectomy in patients with acute ischemic stroke. METHODS We retrospectively identified consecutive patients with acute ischemic stroke who were treated with endovascular thrombectomy within 24 hours of the last-known-well time and achieved successful recanalization (modified Thrombolysis in Cerebral Infarction score ≥2b). Relative DWI signal intensity was calculated as DWI signal intensity in the infarcted area divided by DWI signal intensity in the contralateral hemisphere. Good prognosis was defined as a modified Rankin Scale score of 0-2 at 90 days after stroke onset (good prognosis group). RESULTS 49 patients were included in the analysis. Relative DWI signal intensity was significantly lower in the group with good prognosis than in the those with poor prognosis (median (IQR) 1.32 (1.27-1.44) vs 1.56 (1.43-1.66); p<0.01), and the critical cut-off value for predicting good prognosis was 1.449 (area under the curve 0.78). Multiple logistic regression analysis revealed association of good prognosis after endovascular thrombectomy with low relative DWI signal intensity (OR=6.84; 95% CI 1.13 to 41.3; p=0.04). CONCLUSIONS Low relative DWI signal intensity was associated with good prognosis after endovascular thrombectomy. Its ability to predict good clinical outcome shows potential for determining patient suitability for endovascular thrombectomy.
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Affiliation(s)
- Fumihisa Kishi
- Department of Neurosurgery, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
| | - Ichiro Nakagawa
- Department of Neurosurgery, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
| | - HunSoo Park
- Department of Neurosurgery, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
| | - Masashi Kotsugi
- Department of Neurosurgery, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
| | - Kaoru Myouchin
- Department of Radiology, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
| | - Yasushi Motoyama
- Department of Neurosurgery, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
| | - Hiroyuki Nakase
- Department of Neurosurgery, Nara Medical University School of Medicine Graduate School of Medicine, Kashihara, Nara, Japan
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3
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McGarry BL, Damion RA, Chew I, Knight MJ, Harston GW, Carone D, Jezzard P, Sitaram A, Muir KW, Clatworthy P, Kauppinen RA. A Comparison of T 2 Relaxation-Based MRI Stroke Timing Methods in Hyperacute Ischemic Stroke Patients: A Pilot Study. J Cent Nerv Syst Dis 2020; 12:1179573520943314. [PMID: 32963473 PMCID: PMC7488882 DOI: 10.1177/1179573520943314] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 06/29/2020] [Indexed: 12/25/2022] Open
Abstract
Background: T2 relaxation-based magnetic resonance imaging (MRI) signals may provide onset time for acute ischemic strokes with an unknown onset. The ability of visual and quantitative MRI-based methods in a cohort of hyperacute ischemic stroke patients was studied. Methods: A total of 35 patients underwent 3T (3 Tesla) MRI (<9-hour symptom onset). Diffusion-weighted (DWI), apparent diffusion coefficient (ADC), T1-weighted (T1w), T2-weighted (T2w), and T2 relaxation time (T2) images were acquired. T2-weighted fluid attenuation inversion recovery (FLAIR) images were acquired for 17 of these patients. Image intensity ratios of the average intensities in ischemic and non-ischemic reference regions were calculated for ADC, DWI, T2w, T2 relaxation, and FLAIR images, and optimal image intensity ratio cut-offs were determined. DWI and FLAIR images were assessed visually for DWI/FLAIR mismatch. Results: The T2 relaxation time image intensity ratio was the only parameter with significant correlation with stroke duration (r = 0.49, P = .003), an area under the receiver operating characteristic curve (AUC = 0.77, P < .0001), and an optimal cut-off (T2 ratio = 1.072) that accurately identified patients within the 4.5-hour thrombolysis treatment window with sensitivity of 0.74 and specificity of 0.74. In the patients with the additional FLAIR, areas under the precision-recall-gain curve (AUPRG) and F1 scores showed that the T2 relaxation time ratio (AUPRG = 0.60, F1 = 0.73) performed considerably better than the FLAIR ratio (AUPRG = 0.39, F1 = 0.57) and the visual DWI/FLAIR mismatch (F1 = 0.25). Conclusions: Quantitative T2 relaxation time is the preferred MRI parameter in the assessment of patients with unknown onset for treatment stratification.
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Affiliation(s)
- Bryony L McGarry
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Robin A Damion
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Isabel Chew
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Michael J Knight
- School of Psychological Science, University of Bristol, Bristol, UK
| | - George Wj Harston
- Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Davide Carone
- Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Peter Jezzard
- Acute Stroke Programme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Amith Sitaram
- Institute of Neuroscience and Psychology, Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
| | - Keith W Muir
- Institute of Neuroscience and Psychology, Queen Elizabeth University Hospital, University of Glasgow, Glasgow, UK
| | - Philip Clatworthy
- Stroke Neurology, Southmead Hospital, North Bristol NHS Trust, Bristol, UK
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Wouters A, Dupont P, Christensen S, Norrving B, Laage R, Thomalla G, Kemp S, Lansberg M, Thijs V, Albers GW, Lemmens R. Multimodal magnetic resonance imaging to identify stroke onset within 6 h in patients with large vessel occlusions. Eur Stroke J 2019; 3:185-192. [PMID: 31008349 DOI: 10.1177/2396987317753486] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/05/2017] [Indexed: 11/17/2022] Open
Abstract
Introduction Mechanical thrombectomy within 6 h after stroke onset improves the outcome in patients with large vessel occlusions. The aim of our study was to establish a model based on diffusion weighted and perfusion weighted imaging to provide an accurate prediction for the 6 h time-window in patients with unknown time of stroke onset. Patients and methods A predictive model was designed based on data from the DEFUSE 2 study and validated in a subgroup of patients with large vessel occlusions from the AXIS 2 trial. Results We constructed the model in 91 patients from DEFUSE 2. The following parameters were independently associated with <6 h time-window and included in the model: interquartile range and median relative diffusion weighted imaging, hypoperfusion intensity ratio, core volume and the interaction between median relative diffusion weighted imaging and hypoperfusion intensity ratio as predictors of the 6 h time-window. The area under the curve was 0.80 with a positive predictive value of 0.90 (95%CI 0.79-0.96). In the validation cohort (N = 90), the area under the curve was 0.73 (P for difference = 0.4) with a positive predictive value of 0.85 (95%CI 0.69-0.95). Discussion After validation in a larger independent dataset the model can be considered to select patients for endovascular treatment in whom stroke onset is unknown. Conclusion In patients with large vessel occlusion and unknown time of stroke onset an automated multivariate imaging model is able to select patients who are likely within the 6 h time-window.
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Affiliation(s)
- Anke Wouters
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium.,Center for Brain & Disease Research, Laboratory of Neurobiology, VIB, Leuven, Belgium.,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Soren Christensen
- Stanford Stroke Center, Stanford University Medical Center, Palo Alto, USA
| | - Bo Norrving
- Department of Clinical Sciences, Section of Neurology, Lund University, Lund, Sweden
| | - Rico Laage
- Guided Development GmbH, Heidelberg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephanie Kemp
- Stanford Stroke Center, Stanford University Medical Center, Palo Alto, USA
| | - Maarten Lansberg
- Stanford Stroke Center, Stanford University Medical Center, Palo Alto, USA
| | - Vincent Thijs
- 9Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
| | - Gregory W Albers
- Stanford Stroke Center, Stanford University Medical Center, Palo Alto, USA
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium.,Center for Brain & Disease Research, Laboratory of Neurobiology, VIB, Leuven, Belgium.,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
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5
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Da Ros V, Cortese J, Chassin O, Rouchaud A, Sarov M, Caroff J, Mihalea C, Minosse S, Taifas I, Scaggiante J, Greco L, Ikka L, Ben Achour N, Di Giuliano F, Ozanne A, Legris N, Diomedi M, Sallustio F, Floris R, Denier C, Spelle L. Thrombectomy or intravenous thrombolysis in patients with NIHSS of 5 or less? J Neuroradiol 2019; 46:225-230. [PMID: 30659890 DOI: 10.1016/j.neurad.2019.01.089] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 01/03/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND AND PURPOSE To compare outcomes of minor stroke patients with intracranial vessel occlusions (IVO) underwent mechanical thrombectomy (MT) versus those treated with intravenous thrombolysis alone (IVT). METHODS We retrospectively reviewed two large prospective stroke databases from two European centers searching for patients admitted with minor stroke (i.e. NIHSS Score░≤░5), baseline mRS░=░0 and occlusion of the M1-M2 segment of the middle cerebral artery (MCA). Groups receiving (A) IVT alone and (B) MT+/-IVT were compared. Primary outcome measures were MT safety, successful recanalization rate (mTICI 2b-3) and NIHSS shift (discharge NIHSS minus admission NIHSS); secondary outcomes included discharge rates and excellent outcome (mRS 0-1) at 3 months. Univariate and multivariate analyses were performed. RESULTS Thirty-two patients were enrolled in Group B (19░MT alone; 13 MT░+░IVT) and 24 in Group A. Successful recanalization (mTICI 2b-3) was obtained in 100% of cases in Group B vs 38% in Group A. Symptomatic hemorrhagic transformation rate did not differ between the two groups. Multivariate analysis reported MT as the only predictor of early (<░12░h) favorable NIHSS shift and lower NIHSS at discharge. Moreover, discharge at home and excellent outcome at 3-month follow-up were statistically associated with MT. CONCLUSIONS MT in patients with minor strokes and intracranial vessel occlusion (IVO) is safe and can determine a rapid improvement of NIHSS Score. MT seems also associated with a higher rate of patients discharged at home after hospitalization and better clinical outcome at 3-month follow-up. Larger randomized trials are warranted to confirm these results.
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Affiliation(s)
- Valerio Da Ros
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy; Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France.
| | - Jonathan Cortese
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Olivier Chassin
- Department of Neurology, Bicêtre Hospital, 78, rue du Général-Leclerc, 94270 Le Kremlin-Bicêtre, Paris, France
| | - Aymeric Rouchaud
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Mariana Sarov
- Department of Neurology, Bicêtre Hospital, 78, rue du Général-Leclerc, 94270 Le Kremlin-Bicêtre, Paris, France
| | - Jildaz Caroff
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Cristian Mihalea
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy; Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Silvia Minosse
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Irina Taifas
- Department of Neurology, Bicêtre Hospital, 78, rue du Général-Leclerc, 94270 Le Kremlin-Bicêtre, Paris, France
| | - Jacopo Scaggiante
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Laura Greco
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Leon Ikka
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Nidhal Ben Achour
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Francesca Di Giuliano
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Augustin Ozanne
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
| | - Nicolas Legris
- Department of Neurology, Bicêtre Hospital, 78, rue du Général-Leclerc, 94270 Le Kremlin-Bicêtre, Paris, France
| | - Marina Diomedi
- Department of Neurology, Stroke Unit, "Tor Vergata" University Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Fabrizio Sallustio
- Department of Neurology, Stroke Unit, "Tor Vergata" University Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Roberto Floris
- Department of diagnostic imaging and interventional radiology, "Tor Vergata" university Hospital, viale Oxford, 81, 00133 Rome, Italy
| | - Christian Denier
- Department of Neurology, Bicêtre Hospital, 78, rue du Général-Leclerc, 94270 Le Kremlin-Bicêtre, Paris, France
| | - Laurent Spelle
- Interventional Neuroradiology NEURI Center, Hôpital Bicêtre, 78, rue du Général-Leclerc, 94270 Le Kremlin Bicêtre, France
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Galinovic I, Dicken V, Heitz J, Klein J, Puig J, Guibernau J, Kemmling A, Gellissen S, Villringer K, Neeb L, Gregori J, Weiler F, Pedraza S, Thomalla G, Fiehler J, Gerloff C, Fiebach JB. Homogeneous application of imaging criteria in a multicenter trial supported by investigator training: A report from the WAKE-UP study. Eur J Radiol 2018; 104:115-119. [DOI: 10.1016/j.ejrad.2018.05.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 03/27/2018] [Accepted: 05/10/2018] [Indexed: 10/16/2022]
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7
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Wouters A, Cheng B, Christensen S, Dupont P, Robben D, Norrving B, Laage R, Thijs VN, Albers GW, Thomalla G, Lemmens R. Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours. Neurology 2018; 90:e1570-e1577. [DOI: 10.1212/wnl.0000000000005413] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 02/05/2018] [Indexed: 11/15/2022] Open
Abstract
ObjectiveTo develop an automated model based on diffusion-weighted imaging (DWI) to detect patients within 4.5 hours after stroke onset and compare this method to the visual DWI-FLAIR (fluid-attenuated inversion recovery) mismatch.MethodsWe performed a subanalysis of the “DWI-FLAIR mismatch for the identification of patients with acute ischemic stroke within 4.5 hours of symptom onset” (PRE-FLAIR) and the “AX200 for ischemic stroke” (AXIS 2) trials. We developed a prediction model with data from the PRE-FLAIR study by backward logistic regression with the 4.5-hour time window as dependent variable and the following explanatory variables: age and median relative DWI (rDWI) signal intensity, interquartile range (IQR) rDWI signal intensity, and volume of the core. We obtained the accuracy of the model to predict the 4.5-hour time window and validated our findings in an independent cohort from the AXIS 2 trial. We compared the receiver operating characteristic curve to the visual DWI-FLAIR mismatch.ResultsIn the derivation cohort of 118 patients, we retained the IQR rDWI as explanatory variable. A threshold of 0.39 was most optimal in selecting patients within 4.5 hours after stroke onset resulting in a sensitivity of 76% and specificity of 63%. The accuracy was validated in an independent cohort of 200 patients. The predictive value of the area under the curve of 0.72 (95% confidence interval 0.64–0.80) was similar to the visual DWI-FLAIR mismatch (area under the curve = 0.65; 95% confidence interval 0.58–0.72; p for difference = 0.18).ConclusionsAn automated analysis of DWI performs at least as good as the visual DWI-FLAIR mismatch in selecting patients within the 4.5-hour time window.
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8
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Temporal evolution of the signal intensity of hyper-acute ischemic lesions in a canine stroke model: influence of hyperintense acute reperfusion marker. Jpn J Radiol 2017; 35:161-167. [DOI: 10.1007/s11604-017-0615-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 01/14/2017] [Indexed: 10/20/2022]
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9
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McGarry BL, Rogers HJ, Knight MJ, Jokivarsi KT, Gröhn OH, Kauppinen RA. Determining Stroke Onset Time Using Quantitative MRI: High Accuracy, Sensitivity and Specificity Obtained from Magnetic Resonance Relaxation Times. Cerebrovasc Dis Extra 2016. [PMCID: PMC5040899 DOI: 10.1159/000448814] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Many ischaemic stroke patients are ineligible for thrombolytic therapy due to unknown onset time. Quantitative MRI (qMRI) is a potential surrogate for stroke timing. Rats were subjected to permanent middle cerebral artery occlusion and qMRI parameters including hemispheric differences in apparent diffusion coefficient, T2-weighted signal intensities, T1 and T2 relaxation times (qT1, qT2) and f1, f2 and Voverlap were measured at hourly intervals at 4.7 or 9.4 T. Accuracy and sensitivity for identifying strokes scanned within and beyond 3 h of onset was determined. Accuracy for Voverlap, f2 and qT2 (>90%) was significantly higher than other parameters. At a specificity of 1, sensitivity was highest for Voverlap (0.90) and f2 (0.80), indicating promise of these qMRI indices in the clinical assessment of stroke onset time.
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Affiliation(s)
- Bryony L. McGarry
- School of Experimental Psychology, University of Bristol, London, UK
- *Bryony L. McGarry, School of Experimental Psychology, University of Bristol, 12a Priory Road, Clifton, Bristol BS8 1TU (UK), E-Mail
| | - Harriet J. Rogers
- Imaging and Biophysics, Institute of Child Health, University College London, London, UK
| | - Michael J. Knight
- School of Experimental Psychology, University of Bristol, London, UK
| | - Kimmo T. Jokivarsi
- Department of Neurobiology, University of Eastern Finland, Kuopio, Finland
| | - Olli H.J. Gröhn
- Department of Neurobiology, University of Eastern Finland, Kuopio, Finland
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