BACKGROUND: The utility of admission CT perfusion (CTP) to that of diffusion-weighted imaging (DWI) as a predictor of hemorrhagic transformation (HT) in acute stroke was compared. METHODS: We analyzed the admission CTP and DWI scans of 96 consecutive stroke patients. HT was present in 22 patients (23%). Infarct core was manually segmented on the admission DWI. We determined the: (1) hypoperfused tissue volume in the ischemic hemisphere using a range of thresholds applied to multiple different CTP parameter maps, and (2) mean relative CTP (rCTP) voxel values within both the DWI-segmented lesions and the thresholded CTP parameter maps. Receiver operating characteristic area under curve (AUC) analysis and multivariate regression were used to evaluate the test characteristics of each set of volumes and mean rCTP parameter values as predictors of HT. RESULTS: The hypoperfused tissue volumes with either relative cerebral blood flow (rCBF) <0.48 (AUC = 0.73), or relative mean transit time (rMTT) >1.3 (AUC = 0.70), had similar accuracy to the DWI-segmented core volume (AUC = 0.68, p = 0.2 and p = 0.1, respectively) as predictors of HT. The mean rMTT voxel values within the rMTT >1.3 segmented lesion (AUC = 0.71) had similar accuracy to the mean rMTT voxel values (AUC = 0.65, p = 0.24) and mean rCBF voxel values (AUC = 0.64, p = 0.22) within the DWI-segmented lesion. The only independent predictors of HT were: (1) mean rMTT with rMTT >1.3, and (2) mechanical thrombectomy. CONCLUSION: Admission CTP-based hypoperfused tissue volumes and thresholded mean voxel values are markers of HT in acute stroke, with similar accuracy to DWI. This could be of value when MRI cannot be obtained.
BACKGROUND: The utility of admission CT perfusion (CTP) to that of diffusion-weighted imaging (DWI) as a predictor of hemorrhagic transformation (HT) in acute stroke was compared. METHODS: We analyzed the admission CTP and DWI scans of 96 consecutive strokepatients. HT was present in 22 patients (23%). Infarct core was manually segmented on the admission DWI. We determined the: (1) hypoperfused tissue volume in the ischemic hemisphere using a range of thresholds applied to multiple different CTP parameter maps, and (2) mean relative CTP (rCTP) voxel values within both the DWI-segmented lesions and the thresholded CTP parameter maps. Receiver operating characteristic area under curve (AUC) analysis and multivariate regression were used to evaluate the test characteristics of each set of volumes and mean rCTP parameter values as predictors of HT. RESULTS: The hypoperfused tissue volumes with either relative cerebral blood flow (rCBF) <0.48 (AUC = 0.73), or relative mean transit time (rMTT) >1.3 (AUC = 0.70), had similar accuracy to the DWI-segmented core volume (AUC = 0.68, p = 0.2 and p = 0.1, respectively) as predictors of HT. The mean rMTT voxel values within the rMTT >1.3 segmented lesion (AUC = 0.71) had similar accuracy to the mean rMTT voxel values (AUC = 0.65, p = 0.24) and mean rCBF voxel values (AUC = 0.64, p = 0.22) within the DWI-segmented lesion. The only independent predictors of HT were: (1) mean rMTT with rMTT >1.3, and (2) mechanical thrombectomy. CONCLUSION: Admission CTP-based hypoperfused tissue volumes and thresholded mean voxel values are markers of HT in acute stroke, with similar accuracy to DWI. This could be of value when MRI cannot be obtained.
Authors: Jens Fiehler; Christian Remmele; Thomas Kucinski; Michael Rosenkranz; Götz Thomalla; Cornelius Weiller; Hermann Zeumer; Joachim Röther Journal: Cerebrovasc Dis Date: 2005-01-06 Impact factor: 2.762
Authors: J Hom; J W Dankbaar; B P Soares; T Schneider; S-C Cheng; J Bredno; B C Lau; W Smith; W P Dillon; M Wintermark Journal: AJNR Am J Neuroradiol Date: 2010-10-14 Impact factor: 3.825
Authors: Bruce C V Campbell; Søren Christensen; Kenneth S Butcher; Ian Gordon; Mark W Parsons; Patricia M Desmond; P Alan Barber; Christopher R Levi; Christopher F Bladin; Deidre A De Silva; Geoffrey A Donnan; Stephen M Davis Journal: Stroke Date: 2009-12-03 Impact factor: 7.914
Authors: Chelsea S Kidwell; Larry Latour; Jeffrey L Saver; Jeffry R Alger; Sidney Starkman; Gary Duckwiler; Reza Jahan; Fernando Vinuela; Dong-Wha Kang; Steven Warach Journal: Cerebrovasc Dis Date: 2008-02-27 Impact factor: 2.762
Authors: Richard I Aviv; Christopher D d'Esterre; Blake D Murphy; Julia J Hopyan; Brian Buck; Gabriella Mallia; Vivian Li; Liying Zhang; Sean P Symons; Ting-Yim Lee Journal: Radiology Date: 2009-03 Impact factor: 11.105
Authors: Alexander D Horsch; Edwin Bennink; Tom van Seeters; L Jaap Kappelle; Yolanda van der Graaf; Willem P T M Mali; Hugo W A M de Jong; Birgitta K Velthuis; Jan Willem Dankbaar Journal: Cerebrovasc Dis Date: 2018-01-08 Impact factor: 2.762
Authors: Anderson Chun On Tsang; Stephanie Lenck; Christopher Hilditch; Patrick Nicholson; Waleed Brinjikji; Timo Krings; Vitor M Pereira; Frank L Silver; Joanna D Schaafsma Journal: Clin Neuroradiol Date: 2018-11-23 Impact factor: 3.649
Authors: A R Jain; M Jain; A R Kanthala; D Damania; L G Stead; H Z Wang; B S Jahromi Journal: AJNR Am J Neuroradiol Date: 2013-04-18 Impact factor: 3.825
Authors: Won Hyung A Ryu; Michael B Avery; Navjit Dharampal; Isabel E Allen; Steven W Hetts Journal: J Neurointerv Surg Date: 2016-11-09 Impact factor: 5.836