title: Computer-assisted volumetric measurement of core infarct volume in pediatric patients: feasibility for clinical use and development of quantitative metrics for outcome prediction.
abstract: OBJECTIVE
  Infarct volume may predict clinical outcome in acute stroke, but manual segmentation techniques limit its routine use. We hypothesized that computer-assisted volumetric analysis to quantify acute infarct volume will show no difference compared with manual segmentation but will show increased speed of performance and will correlate with outcome.
  
  
  METHODS
  Patients with acute stroke younger than 18 years were included. Infarct volume on diffusion-weighted imaging was quantified by using computer-assisted volumetric and manual techniques. The Pediatric Stroke Outcome Measure scored clinical outcome. Computer-assisted volumetric and manual techniques were compared with correlation coefficients. Linear regression analysis compared Pediatric Stroke Outcome Measure with core infarct volume and percentage volume of brain infarction.
  
  
  RESULTS
  Twenty-three patients were analyzed (mean age, 4.6 years). Mean infarct volume from computer-assisted volumetric and manual approaches was 65.6 and 63.7 mL, respectively (P = .56). Concordance correlation between methods was 0.980, and between users, 0.968. The mean times for segmentation between computer-assisted volumetric and manual techniques were <1 minute and 7.3 minutes (P < .001). The mean infarct volumes for good and poor outcome groups were 7.4 and 75.7 mL (P < .007). The mean percentages of infarcted brain parenchyma for good and poor outcome groups were 0.6% and 10.4% (P < .006). Volumes of 32 mL and 3% for infarcted brain were associated with poor outcome in all patients.
  
  
  CONCLUSIONS
  Computer-assisted volumetric quantification of infarct volume is reproducible, is significantly faster than manual techniques, and may have important applications for future clinical workflow. Core infarct volumes and infarct percentage correlated with outcome severity.
authors.affiliation: From the Department of Radiology, Columbia University Medical Center, New York, New York. sairaallapeikko@gmail.com.
authors.forename: C G
authors.initials: CG
authors.lastname: Filippi
authors.affiliation: From the Department of Radiology, Columbia University Medical Center, New York, New York.
authors.forename: A M
authors.initials: AM
authors.lastname: El-Ali
authors.affiliation: From the Department of Radiology, Columbia University Medical Center, New York, New York.
authors.forename: V Z
authors.initials: VZ
authors.lastname: Miloushev
authors.affiliation: From the Department of Radiology, Columbia University Medical Center, New York, New York.
authors.forename: D S
authors.initials: DS
authors.lastname: Chow
authors.affiliation: From the Department of Radiology, Columbia University Medical Center, New York, New York.
authors.forename: X
authors.initials: X
authors.lastname: Guo
authors.affiliation: From the Department of Radiology, Columbia University Medical Center, New York, New York.
authors.forename: B
authors.initials: B
authors.lastname: Zhao
mesh_terms: D020520:Brain Infarction / Q000473:pathology*; D002648:Child; D002675:Child, Preschool; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D011379:Prognosis; D020127:Recovery of Function; D015203:Reproducibility of Results; D020521:Stroke / Q000473:pathology*