title: Gray-Matter Volume Estimate Score: A Novel Semi-Automatic Method Measuring Early Ischemic Change on CT.
abstract: OBJECTIVE
  We developed a novel method named Gray-matter Volume Estimate Score (GRAVES), measuring early ischemic changes on Computed Tomography (CT) semi-automatically by computer software. This study aimed to compare GRAVES and Alberta Stroke Program Early CT Score (ASPECTS) with regards to outcome prediction and inter-rater agreement.
  
  
  METHODS
  This was a retrospective cohort study. Among consecutive patients with ischemic stroke in the anterior circulation who received intra-arterial therapy (IAT), those with a readable pretreatment CT were included. Two stroke neurologists independently measured both the GRAVES and ASPECTS. GRAVES was defined as the percentage of estimated hypodense lesion in the gray matter of the ipsilateral hemisphere. Spearman correlation analysis, receiver operating characteristic (ROC) comparison test, and intra-class correlation coefficient (ICC) comparison tests were performed between GRAVES and ASPECTS.
  
  
  RESULTS
  Ninety-four subjects (age: 68.7±10.3; male: 54 [54.9%]) were enrolled. The mean GRAVES was 9.0±8.9 and the median ASPECTS was 8 (interquartile range, 6-9). Correlation between ASPECTS and GRAVES was good (Spearman's rank correlation coefficient, 0.642; P<0.001). ROC comparison analysis showed that the predictive value of GRAVES for favorable outcome was not significantly different from that of ASPECTS (area under curve, 0.765 vs. 0.717; P=0.308). ICC comparison analysis revealed that inter-rater agreement of GRAVES was significantly better than that of ASPECTS (0.978 vs. 0.895; P<0.001).
  
  
  CONCLUSIONS
  GRAVES had a good correlation with ASPECTS. GRAVES was as good as ASPECTS in predicting a favorable clinical outcome, but was better than ASPECTS regarding inter-rater agreement. GRAVES may be used to predict the outcome of IAT.
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Dongbeom
authors.initials: D
authors.lastname: Song
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Kijeong
authors.initials: K
authors.lastname: Lee
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Eun Hye
authors.initials: EH
authors.lastname: Kim
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Young Dae
authors.initials: YD
authors.lastname: Kim
authors.affiliation: Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Hye Sun
authors.initials: HS
authors.lastname: Lee
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.|Department of Neurology, CHA Bundang Medical Center, CHA University, Seongnam, Korea.
authors.forename: Jinkwon
authors.initials: J
authors.lastname: Kim
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.|Department of Neurology, Ewha women's University School of Medicine, Seoul, Korea.
authors.forename: Tae-Jin
authors.initials: TJ
authors.lastname: Song
authors.affiliation: Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Sung Soo
authors.initials: SS
authors.lastname: Ahn
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Hyo Suk
authors.initials: HS
authors.lastname: Nam
authors.affiliation: Department of Neurology, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Ji Hoe
authors.initials: JH
authors.lastname: Heo
keywords: ASPECTS; Acute ischemic stroke; Acute stroke imaging; Computed tomography; Prediction of outcome