title: Admission CT perfusion is an independent predictor of hemorrhagic transformation in acute stroke with similar accuracy to DWI.
abstract: 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.
  
  
  CONCLUSIONS
  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.affiliation: Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass., USA.
authors.forename: Leticia C S
authors.initials: LC
authors.lastname: Souza
authors.forename: Seyedmehdi
authors.initials: S
authors.lastname: Payabvash
authors.forename: Yifei
authors.initials: Y
authors.lastname: Wang
authors.forename: Shervin
authors.initials: S
authors.lastname: Kamalian
authors.forename: Pamela
authors.initials: P
authors.lastname: Schaefer
authors.forename: R Gilberto
authors.initials: RG
authors.lastname: Gonzalez
authors.forename: Karen L
authors.initials: KL
authors.lastname: Furie
authors.forename: Michael H
authors.initials: MH
authors.lastname: Lev
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001900:Boston; D002560:Cerebrovascular Circulation; D016009:Chi-Square Distribution; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D006801:Humans; D020300:Intracranial Hemorrhages / Q000209:etiology* / Q000473:pathology / Q000503:physiopathology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010343:Patient Admission; D055420:Perfusion Imaging / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D020521:Stroke / Q000150:complications / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D014057:Tomography, X-Ray Computed