MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification
In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC). We enrolle...
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| Published in | Journal of stroke Vol. 16; no. 3; pp. 161 - 172 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Korea (South)
Korean Stroke Society
01.09.2014
대한뇌졸중학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2287-6391 1229-4101 2287-6405 |
| DOI | 10.5853/jos.2014.16.3.161 |
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| Abstract | In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC).
We enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions on MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database.
The overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.6%). One-year stroke recurrence rates were the highest for two or more UDs (11.80%), followed by LAA (7.30%), CE (5.60%), and SVO (2.50%).
Despite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic. |
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| AbstractList | In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC).BACKGROUND AND PURPOSEIn order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC).We enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions on MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database.METHODSWe enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions on MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database.The overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.6%). One-year stroke recurrence rates were the highest for two or more UDs (11.80%), followed by LAA (7.30%), CE (5.60%), and SVO (2.50%).RESULTSThe overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.6%). One-year stroke recurrence rates were the highest for two or more UDs (11.80%), followed by LAA (7.30%), CE (5.60%), and SVO (2.50%).Despite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic.CONCLUSIONSDespite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic. Background and PurposeIn order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC).MethodsWe enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions on MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database.ResultsThe overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.6%). One-year stroke recurrence rates were the highest for two or more UDs (11.80%), followed by LAA (7.30%), CE (5.60%), and SVO (2.50%).ConclusionsDespite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic. Background and Purpose In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC). Methods We enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions on MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database. Results The overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.6%). One-year stroke recurrence rates were the highest for two or more UDs (11.80%), followed by LAA (7.30%), CE (5.60%), and SVO (2.50%). Conclusions Despite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic. KCI Citation Count: 7 In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we developed and implemented a magnetic resonance imaging (MRI)-based algorithm for acute ischemic stroke subtype classification (MAGIC). We enrolled patients who experienced an acute ischemic stroke, were hospitalized in the 14 participating centers within 7 days of onset, and had relevant lesions on MR-diffusion weighted imaging (DWI). MAGIC was designed to reflect recent advances in stroke imaging and thrombolytic therapy. The inter-rater reliability was compared with and without MAGIC to classify the Trial of Org 10172 in Acute Stroke Treatment (TOAST) of each stroke patient. MAGIC was then applied to all stroke patients hospitalized since July 2011, and information about stroke subtypes, other clinical characteristics, and stroke recurrence was collected via a web-based registry database. The overall intra-class correlation coefficient (ICC) value was 0.43 (95% CI, 0.31-0.57) for MAGIC and 0.28 (95% CI, 0.18-0.42) for TOAST. Large artery atherosclerosis (LAA) was the most common cause of acute ischemic stroke (38.3%), followed by cardioembolism (CE, 22.8%), undetermined cause (UD, 22.2%), and small-vessel occlusion (SVO, 14.6%). One-year stroke recurrence rates were the highest for two or more UDs (11.80%), followed by LAA (7.30%), CE (5.60%), and SVO (2.50%). Despite several limitations, this study shows that the MAGIC system is feasible and may be helpful to classify stroke subtype in the clinic. |
| Author | Hong, Keun-Sik Lee, Jun Yu, Kyung-Ho Lee, Ji Sung Lee, Byung-Chul Han, Moon-Ku Bae, Hee-Joon Lee, Juneyoung Park, Sang-Soon Park, Jong-Moo Chung, Jong-Won Choi, Jay Chol Kang, Kyusik Cho, Yong-Jin Kim, Dong-Eog Ko, Youngchai Kim, Joon-Tae Park, Tai Hwan Lee, SooJoo Kim, Dae-Hyun Cha, Jae-Kwan Shin, Dong-Ick Lee, Kyung Bok |
| AuthorAffiliation | i Department of Neurology, Dong-A University Hospital, Busan, Korea k Department of Neurology, Jeju National University Hospital, Jeju National University School of Medicine, Jeju, Korea l Department of Neurology, Chungbuk National University College of Medicine, Cheongju, Korea g Department of Neurology, Yeungnam University Hospital, Daegu, Korea h Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea n Department of Biostatistics, Korea University College of Medicine, Seoul, Korea m Biostatistical Consulting Unit, Soonchunhyang University Medical Center, Seoul, Korea b Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea d Department of Neurology, Seoul Medical Center, Seoul, Korea e Department of Neurology, Ilsan Paik Hospital, Inje University, Goyang, Korea a Department of Neurology, Eulji University Hospital, Eulji University, Daejeon, Korea c Department of Neurology, Eulji General Hospital, Eulji University, Seoul, |
| AuthorAffiliation_xml | – name: o Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea – name: g Department of Neurology, Yeungnam University Hospital, Daegu, Korea – name: f Department of Neurology, Soonchunhyang University Hospital, Seoul, Korea – name: k Department of Neurology, Jeju National University Hospital, Jeju National University School of Medicine, Jeju, Korea – name: l Department of Neurology, Chungbuk National University College of Medicine, Cheongju, Korea – name: e Department of Neurology, Ilsan Paik Hospital, Inje University, Goyang, Korea – name: j Department of Neurology, Chonnam National University Hospital, Gwangju, Korea – name: h Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea – name: c Department of Neurology, Eulji General Hospital, Eulji University, Seoul, Korea – name: m Biostatistical Consulting Unit, Soonchunhyang University Medical Center, Seoul, Korea – name: a Department of Neurology, Eulji University Hospital, Eulji University, Daejeon, Korea – name: b Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea – name: d Department of Neurology, Seoul Medical Center, Seoul, Korea – name: i Department of Neurology, Dong-A University Hospital, Busan, Korea – name: n Department of Biostatistics, Korea University College of Medicine, Seoul, Korea |
| Author_xml | – sequence: 1 givenname: Youngchai surname: Ko fullname: Ko, Youngchai organization: Department of Neurology, Eulji University Hospital, Eulji University, Daejeon, Korea – sequence: 2 givenname: SooJoo surname: Lee fullname: Lee, SooJoo organization: Department of Neurology, Eulji University Hospital, Eulji University, Daejeon, Korea – sequence: 3 givenname: Jong-Won surname: Chung fullname: Chung, Jong-Won organization: Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea – sequence: 4 givenname: Moon-Ku surname: Han fullname: Han, Moon-Ku organization: Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea – sequence: 5 givenname: Jong-Moo surname: Park fullname: Park, Jong-Moo organization: Department of Neurology, Eulji General Hospital, Eulji University, Seoul, Korea – sequence: 6 givenname: Kyusik surname: Kang fullname: Kang, Kyusik organization: Department of Neurology, Eulji General Hospital, Eulji University, Seoul, Korea – sequence: 7 givenname: Tai Hwan surname: Park fullname: Park, Tai Hwan organization: Department of Neurology, Seoul Medical Center, Seoul, Korea – sequence: 8 givenname: Sang-Soon surname: Park fullname: Park, Sang-Soon organization: Department of Neurology, Seoul Medical Center, Seoul, Korea – sequence: 9 givenname: Yong-Jin surname: Cho fullname: Cho, Yong-Jin organization: Department of Neurology, Ilsan Paik Hospital, Inje University, Goyang, Korea – sequence: 10 givenname: Keun-Sik surname: Hong fullname: Hong, Keun-Sik organization: Department of Neurology, Ilsan Paik Hospital, Inje University, Goyang, Korea – sequence: 11 givenname: Kyung Bok surname: Lee fullname: Lee, Kyung Bok organization: Department of Neurology, Soonchunhyang University Hospital, Seoul, Korea – sequence: 12 givenname: Jun surname: Lee fullname: Lee, Jun organization: Department of Neurology, Yeungnam University Hospital, Daegu, Korea – sequence: 13 givenname: Dong-Eog surname: Kim fullname: Kim, Dong-Eog organization: Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea – sequence: 14 givenname: Dae-Hyun surname: Kim fullname: Kim, Dae-Hyun organization: Department of Neurology, Dong-A University Hospital, Busan, Korea – sequence: 15 givenname: Jae-Kwan surname: Cha fullname: Cha, Jae-Kwan organization: Department of Neurology, Dong-A University Hospital, Busan, Korea – sequence: 16 givenname: Joon-Tae surname: Kim fullname: Kim, Joon-Tae organization: Department of Neurology, Chonnam National University Hospital, Gwangju, Korea – sequence: 17 givenname: Jay Chol surname: Choi fullname: Choi, Jay Chol organization: Department of Neurology, Jeju National University Hospital, Jeju National University School of Medicine, Jeju, Korea – sequence: 18 givenname: Dong-Ick surname: Shin fullname: Shin, Dong-Ick organization: Department of Neurology, Chungbuk National University College of Medicine, Cheongju, Korea – sequence: 19 givenname: Ji Sung surname: Lee fullname: Lee, Ji Sung organization: Biostatistical Consulting Unit, Soonchunhyang University Medical Center, Seoul, Korea – sequence: 20 givenname: Juneyoung surname: Lee fullname: Lee, Juneyoung organization: Department of Biostatistics, Korea University College of Medicine, Seoul, Korea – sequence: 21 givenname: Kyung-Ho surname: Yu fullname: Yu, Kyung-Ho organization: Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea – sequence: 22 givenname: Byung-Chul surname: Lee fullname: Lee, Byung-Chul organization: Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Korea – sequence: 23 givenname: Hee-Joon surname: Bae fullname: Bae, Hee-Joon organization: Department of Neurology, Cerebrovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25328874$$D View this record in MEDLINE/PubMed https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001915163$$DAccess content in National Research Foundation of Korea (NRF) |
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| Issue | 3 |
| Keywords | Stroke Magnetic resonance imaging Algorithm Classification |
| Language | English |
| License | http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| References | Ko (10.5853/jos.2014.16.3.161_ref8) 2010; 29 Bang (10.5853/jos.2014.16.3.161_ref6) 2003; 54 Marnane (10.5853/jos.2014.16.3.161_ref14) 2010; 41 Grau (10.5853/jos.2014.16.3.161_ref21) 2001; 32 Kim (10.5853/jos.2014.16.3.161_ref12) 2012; 8 Shang (10.5853/jos.2014.16.3.161_ref18) 2012; 314 Lee (10.5853/jos.2014.16.3.161_ref17) 2001; 12 Hajat (10.5853/jos.2014.16.3.161_ref19) 2011; 82 Kim (10.5853/jos.2014.16.3.161_ref7) 2014; 9 Gordon (10.5853/jos.2014.16.3.161_ref4) 1993; 43 Levy (10.5853/jos.2014.16.3.161_ref11) 1995; 26 Petty (10.5853/jos.2014.16.3.161_ref13) 2000; 31 Yesilot Barlas (10.5853/jos.2014.16.3.161_ref23) 2013; 20 Adams (10.5853/jos.2014.16.3.161_ref3) 1993; 24 Bang (10.5853/jos.2014.16.3.161_ref15) 2014; 45 Amarenco (10.5853/jos.2014.16.3.161_ref10) 1993; 50 Kolominsky-Rabas (10.5853/jos.2014.16.3.161_ref20) 2001; 32 Meschia (10.5853/jos.2014.16.3.161_ref2) 2006; 15 Deleu (10.5853/jos.2014.16.3.161_ref16) 2011; 300 Kim (10.5853/jos.2014.16.3.161_ref22) 2014; 16 Ay (10.5853/jos.2014.16.3.161_ref1) 2005; 58 Amarenco (10.5853/jos.2014.16.3.161_ref5) 2009; 27 Amarenco (10.5853/jos.2014.16.3.161_ref9) 1990; 21 7709407 - Stroke. 1995 Apr;26(4):609-13 8492920 - Neurology. 1993 May;43(5):1021-7 20595675 - Stroke. 2010 Aug;41(8):1579-86 20974649 - J Neurol Neurosurg Psychiatry. 2011 May;82(5):527-33 11641577 - Cerebrovasc Dis. 2001;12(3):145-51 20875650 - J Neurol Sci. 2011 Jan 15;300(1-2):142-7 22118859 - J Neurol Sci. 2012 Mar 15;314(1-2):66-70 8431134 - Arch Neurol. 1993 Feb;50(2):154-61 20130396 - Cerebrovasc Dis. 2010;29(4):313-20 11692017 - Stroke. 2001 Nov;32(11):2559-66 12891675 - Ann Neurol. 2003 Aug;54(2):227-34 24741560 - J Stroke. 2014 Jan;16(1):8-17 16240340 - Ann Neurol. 2005 Nov;58(5):688-97 23837733 - Eur J Neurol. 2013 Nov;20(11):1431-9 17904086 - J Stroke Cerebrovasc Dis. 2006 Nov-Dec;15(6):266-72 7678184 - Stroke. 1993 Jan;24(1):35-41 22787495 - J Clin Neurol. 2012 Jun;8(2):116-22 2396267 - Stroke. 1990 Sep;21(9):1299-305 19342825 - Cerebrovasc Dis. 2009;27(5):493-501 24578206 - Stroke. 2014 Apr;45(4):1186-94 11739965 - Stroke. 2001 Dec 1;32(12):2735-40 10797166 - Stroke. 2000 May;31(5):1062-8 24256115 - Int J Stroke. 2014 Jun;9(4):514-8 |
| References_xml | – volume: 20 start-page: 1431 year: 2013 ident: 10.5853/jos.2014.16.3.161_ref23 publication-title: Eur J Neurol doi: 10.1111/ene.12228 – volume: 32 start-page: 2735 year: 2001 ident: 10.5853/jos.2014.16.3.161_ref20 publication-title: Stroke doi: 10.1161/hs1201.100209 – volume: 54 start-page: 227 year: 2003 ident: 10.5853/jos.2014.16.3.161_ref6 publication-title: Ann Neurol doi: 10.1002/ana.10644 – volume: 41 start-page: 1579 year: 2010 ident: 10.5853/jos.2014.16.3.161_ref14 publication-title: Stroke doi: 10.1161/STROKEAHA.109.575373 – volume: 29 start-page: 313 year: 2010 ident: 10.5853/jos.2014.16.3.161_ref8 publication-title: Cerebrovasc Dis doi: 10.1159/000278926 – volume: 15 start-page: 266 year: 2006 ident: 10.5853/jos.2014.16.3.161_ref2 publication-title: J Stroke Cerebrovasc Dis doi: 10.1016/j.jstrokecerebrovasdis.2006.07.001 – volume: 21 start-page: 1299 year: 1990 ident: 10.5853/jos.2014.16.3.161_ref9 publication-title: Stroke doi: 10.1161/01.STR.21.9.1299 – volume: 8 start-page: 116 year: 2012 ident: 10.5853/jos.2014.16.3.161_ref12 publication-title: J Clin Neurol doi: 10.3988/jcn.2012.8.2.116 – volume: 45 start-page: 1186 year: 2014 ident: 10.5853/jos.2014.16.3.161_ref15 publication-title: Stroke doi: 10.1161/STROKEAHA.113.003720 – volume: 24 start-page: 35 year: 1993 ident: 10.5853/jos.2014.16.3.161_ref3 publication-title: Stroke doi: 10.1161/01.STR.24.1.35 – volume: 314 start-page: 66 year: 2012 ident: 10.5853/jos.2014.16.3.161_ref18 publication-title: J Neurol Sci doi: 10.1016/j.jns.2011.10.029 – volume: 27 start-page: 493 year: 2009 ident: 10.5853/jos.2014.16.3.161_ref5 publication-title: Cerebrovasc Dis doi: 10.1159/000210432 – volume: 50 start-page: 154 year: 1993 ident: 10.5853/jos.2014.16.3.161_ref10 publication-title: Arch Neurol doi: 10.1001/archneur.1993.00540020032014 – volume: 31 start-page: 1062 year: 2000 ident: 10.5853/jos.2014.16.3.161_ref13 publication-title: Stroke doi: 10.1161/01.STR.31.5.1062 – volume: 300 start-page: 142 year: 2011 ident: 10.5853/jos.2014.16.3.161_ref16 publication-title: J Neurol Sci doi: 10.1016/j.jns.2010.08.023 – volume: 82 start-page: 527 year: 2011 ident: 10.5853/jos.2014.16.3.161_ref19 publication-title: J Neurol Neurosurg Psychiatry doi: 10.1136/jnnp.2010.222919 – volume: 32 start-page: 2559 year: 2001 ident: 10.5853/jos.2014.16.3.161_ref21 publication-title: Stroke doi: 10.1161/hs1101.098524 – volume: 9 start-page: 514 year: 2014 ident: 10.5853/jos.2014.16.3.161_ref7 publication-title: Int J Stroke doi: 10.1111/ijs.12199 – volume: 58 start-page: 688 year: 2005 ident: 10.5853/jos.2014.16.3.161_ref1 publication-title: Ann Neurol doi: 10.1002/ana.20617 – volume: 12 start-page: 145 year: 2001 ident: 10.5853/jos.2014.16.3.161_ref17 publication-title: Cerebrovasc Dis doi: 10.1159/000047697 – volume: 16 start-page: 8 year: 2014 ident: 10.5853/jos.2014.16.3.161_ref22 publication-title: J Stroke doi: 10.5853/jos.2014.16.1.8 – volume: 26 start-page: 609 year: 1995 ident: 10.5853/jos.2014.16.3.161_ref11 publication-title: Stroke doi: 10.1161/01.STR.26.4.609 – volume: 43 start-page: 1021 year: 1993 ident: 10.5853/jos.2014.16.3.161_ref4 publication-title: Neurology doi: 10.1212/WNL.43.5.1021 – reference: 19342825 - Cerebrovasc Dis. 2009;27(5):493-501 – reference: 12891675 - Ann Neurol. 2003 Aug;54(2):227-34 – reference: 24578206 - Stroke. 2014 Apr;45(4):1186-94 – reference: 11692017 - Stroke. 2001 Nov;32(11):2559-66 – reference: 11739965 - Stroke. 2001 Dec 1;32(12):2735-40 – reference: 20875650 - J Neurol Sci. 2011 Jan 15;300(1-2):142-7 – reference: 16240340 - Ann Neurol. 2005 Nov;58(5):688-97 – reference: 20595675 - Stroke. 2010 Aug;41(8):1579-86 – reference: 22787495 - J Clin Neurol. 2012 Jun;8(2):116-22 – reference: 7709407 - Stroke. 1995 Apr;26(4):609-13 – reference: 2396267 - Stroke. 1990 Sep;21(9):1299-305 – reference: 8431134 - Arch Neurol. 1993 Feb;50(2):154-61 – reference: 11641577 - Cerebrovasc Dis. 2001;12(3):145-51 – reference: 20974649 - J Neurol Neurosurg Psychiatry. 2011 May;82(5):527-33 – reference: 17904086 - J Stroke Cerebrovasc Dis. 2006 Nov-Dec;15(6):266-72 – reference: 20130396 - Cerebrovasc Dis. 2010;29(4):313-20 – reference: 24256115 - Int J Stroke. 2014 Jun;9(4):514-8 – reference: 23837733 - Eur J Neurol. 2013 Nov;20(11):1431-9 – reference: 24741560 - J Stroke. 2014 Jan;16(1):8-17 – reference: 8492920 - Neurology. 1993 May;43(5):1021-7 – reference: 7678184 - Stroke. 1993 Jan;24(1):35-41 – reference: 10797166 - Stroke. 2000 May;31(5):1062-8 – reference: 22118859 - J Neurol Sci. 2012 Mar 15;314(1-2):66-70 |
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| Snippet | In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a stroke registry, we... Background and PurposeIn order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a... Background and Purpose In order to improve inter-rater reliability and minimize diagnosis of undetermined etiology for stroke subtype classification, using a... |
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| SubjectTerms | algorithm classification magnetic resonance imaging Original stroke 신경과학 |
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| Title | MRI-based Algorithm for Acute Ischemic Stroke Subtype Classification |
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