Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage

Pascal Salazar, Mario Di Napoli, Mostafa Jafari, Alibay Jafarli, Wendy Ziai, Alexander Petersen, Stephan A. Mayer, Eric M. Bershad, Rahul Damani, Afshin A. Divani

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Background: Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice. Methods: We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012–2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4–6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome. Results: We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76–0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86–0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89–0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76–0.93] for volume-only model to AUC: 0.88 [0.80–0.95] for imaging data models and AUC: 0.92 [0.86–0.98] for imaging plus clinical predictors. Conclusions: Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.

Original languageEnglish (US)
Pages (from-to)539-549
Number of pages11
JournalNeurocritical Care
Issue number2
StatePublished - Apr 1 2020

Bibliographical note

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.

Copyright 2020 Elsevier B.V., All rights reserved.


  • CT density
  • Computed tomography
  • Hematoma shape
  • Hematoma volume
  • Intracerebral hemorrhage
  • Outcomes
  • Radiologic predictors

PubMed: MeSH publication types

  • Journal Article


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