BACKGROUND AND PURPOSE: The aim of this study was to determine if automated MRI analysis software (RAPID) can be used to identify patients with stroke in whom reperfusion is associated with an increased chance of good outcome. METHODS: Baseline diffusion- and perfusion-weighted MRI scans from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution study (DEFUSE; n=74) and the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET; n=100) were reprocessed with RAPID. Based on RAPID-generated diffusion-weighted imaging and perfusion-weighted imaging lesion volumes, patients were categorized according to 3 prespecified MRI profiles that were hypothesized to predict benefit (Target Mismatch), harm (Malignant), and no effect (No Mismatch) from reperfusion. Favorable clinical response was defined as a National Institutes of Health Stroke Scale score of 0 to 1 or a ≥ 8-point improvement on the National Institutes of Health Stroke Scale score at Day 90. RESULTS: In Target Mismatch patients, reperfusion was strongly associated with a favorable clinical response (OR, 5.6; 95% CI, 2.1 to 15.3) and attenuation of infarct growth (10 ± 23 mL with reperfusion versus 40 ± 44 mL without reperfusion; P<0.001). In Malignant profile patients, reperfusion was not associated with a favorable clinical response (OR, 0.74; 95% CI, 0.1 to 5.8) or attenuation of infarct growth (85 ± 74 mL with reperfusion versus 95 ± 79 mL without reperfusion; P=0.7). Reperfusion was also not associated with a favorable clinical response (OR, 1.05; 95% CI, 0.1 to 9.4) or attenuation of lesion growth (10 ± 15 mL with reperfusion versus 17 ± 30 mL without reperfusion; P=0.9) in No Mismatch patients. CONCLUSIONS: MRI profiles that are associated with a differential response to reperfusion can be identified with RAPID. This supports the use of automated image analysis software such as RAPID for patient selection in acute stroke trials.
BACKGROUND AND PURPOSE: The aim of this study was to determine if automated MRI analysis software (RAPID) can be used to identify patients with stroke in whom reperfusion is associated with an increased chance of good outcome. METHODS: Baseline diffusion- and perfusion-weighted MRI scans from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution study (DEFUSE; n=74) and the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET; n=100) were reprocessed with RAPID. Based on RAPID-generated diffusion-weighted imaging and perfusion-weighted imaging lesion volumes, patients were categorized according to 3 prespecified MRI profiles that were hypothesized to predict benefit (Target Mismatch), harm (Malignant), and no effect (No Mismatch) from reperfusion. Favorable clinical response was defined as a National Institutes of Health Stroke Scale score of 0 to 1 or a ≥ 8-point improvement on the National Institutes of Health Stroke Scale score at Day 90. RESULTS: In Target Mismatch patients, reperfusion was strongly associated with a favorable clinical response (OR, 5.6; 95% CI, 2.1 to 15.3) and attenuation of infarct growth (10 ± 23 mL with reperfusion versus 40 ± 44 mL without reperfusion; P<0.001). In Malignant profile patients, reperfusion was not associated with a favorable clinical response (OR, 0.74; 95% CI, 0.1 to 5.8) or attenuation of infarct growth (85 ± 74 mL with reperfusion versus 95 ± 79 mL without reperfusion; P=0.7). Reperfusion was also not associated with a favorable clinical response (OR, 1.05; 95% CI, 0.1 to 9.4) or attenuation of lesion growth (10 ± 15 mL with reperfusion versus 17 ± 30 mL without reperfusion; P=0.9) in No Mismatch patients. CONCLUSIONS: MRI profiles that are associated with a differential response to reperfusion can be identified with RAPID. This supports the use of automated image analysis software such as RAPID for patient selection in acute stroke trials.
Authors: A I Qureshi; J F Kirmani; M A Sayed; A Safdar; S Ahmed; R Ferguson; L A Hershey; K J Qazi Journal: Neurology Date: 2005-06-28 Impact factor: 9.910
Authors: Gregory W Albers; Vincent N Thijs; Lawrence Wechsler; Stephanie Kemp; Gottfried Schlaug; Elaine Skalabrin; Roland Bammer; Wataru Kakuda; Maarten G Lansberg; Ashfaq Shuaib; William Coplin; Scott Hamilton; Michael Moseley; Michael P Marks Journal: Ann Neurol Date: 2006-11 Impact factor: 10.422
Authors: Stephen M Davis; Geoffrey A Donnan; Mark W Parsons; Christopher Levi; Kenneth S Butcher; Andre Peeters; P Alan Barber; Christopher Bladin; Deidre A De Silva; Graham Byrnes; Jonathan B Chalk; John N Fink; Thomas E Kimber; David Schultz; Peter J Hand; Judith Frayne; Graeme Hankey; Keith Muir; Richard Gerraty; Brian M Tress; Patricia M Desmond Journal: Lancet Neurol Date: 2008-02-28 Impact factor: 44.182
Authors: W Hacke; M Kaste; C Fieschi; R von Kummer; A Davalos; D Meier; V Larrue; E Bluhmki; S Davis; G Donnan; D Schneider; E Diez-Tejedor; P Trouillas Journal: Lancet Date: 1998-10-17 Impact factor: 79.321
Authors: Oliver C Singer; Marek C Humpich; Jens Fiehler; Gregory W Albers; Maarten G Lansberg; Andiras Kastrup; Alex Rovira; David S Liebeskind; Achim Gass; Charlotte Rosso; Laurent Derex; Jong S Kim; Tobias Neumann-Haefelin Journal: Ann Neurol Date: 2008-01 Impact factor: 10.422
Authors: Jean-Marc Olivot; Michael Mlynash; Vincent N Thijs; Stephanie Kemp; Maarten G Lansberg; Lawrence Wechsler; Roland Bammer; Michael P Marks; Gregory W Albers Journal: Stroke Date: 2008-12-24 Impact factor: 7.914
Authors: Werner Hacke; Geoffrey Donnan; Cesare Fieschi; Markku Kaste; Rüdiger von Kummer; Joseph P Broderick; Thomas Brott; Michael Frankel; James C Grotta; E Clarke Haley; Thomas Kwiatkowski; Steven R Levine; Chris Lewandowski; Mei Lu; Patrick Lyden; John R Marler; Suresh Patel; Barbara C Tilley; Gregory Albers; Erich Bluhmki; Manfred Wilhelm; Scott Hamilton Journal: Lancet Date: 2004-03-06 Impact factor: 79.321
Authors: Michael P Marks; Maarten G Lansberg; Michael Mlynash; Stephanie Kemp; Ryan A McTaggart; Greg Zaharchuk; Roland Bammer; Gregory W Albers Journal: Int J Stroke Date: 2014-03-31 Impact factor: 5.266
Authors: Hongyu An; Andria L Ford; Yasheng Chen; Hongtu Zhu; Rosana Ponisio; Gyanendra Kumar; Amirali Modir Shanechi; Naim Khoury; Katie D Vo; Jennifer Williams; Colin P Derdeyn; Michael N Diringer; Peter Panagos; William J Powers; Jin-Moo Lee; Weili Lin Journal: Stroke Date: 2015-02-26 Impact factor: 7.914
Authors: Stephen Davis; Bruce Campbell; Soren Christensen; Henry Ma; Patricia Desmond; Mark Parsons; Christopher Levi; Christopher Bladin; P Alan Barber; Geoffrey Donnan Journal: Transl Stroke Res Date: 2012-04-18 Impact factor: 6.829
Authors: Aaryani Tipirneni-Sajja; Soren Christensen; Matus Straka; Manabu Inoue; Maarten G Lansberg; Michael Mlynash; Roland Bammer; Mark W Parsons; Geoffrey A Donnan; Stephen M Davis; Gregory W Albers Journal: J Cereb Blood Flow Metab Date: 2016-01-01 Impact factor: 6.200