ArticleNeurocritical care2020
Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage.
Article in Neurocritical care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Rapidity of hematoma resolution after fibrinolytic therapy for intracerebral hemorrhage has a favorable effect on functional outcome.Scientific reports · 2025Article
- Article
- The relationship between hematoma morphology and intraventricular hemorrhage in supratentorial deep intracerebral hemorrhage.Quantitative imaging in medicine and surgery · 2023Article
- Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients.Scientific reports · 2022Article
- The Patterns of Morphological Change During Intracerebral Hemorrhage Expansion: A Multicenter Retrospective Cohort Study.Frontiers in medicine · 2021Article
- Automated detection of 3D midline shift in spontaneous supratentorial intracerebral haemorrhage with non-contrast computed tomography using deep convolutional neural networks.American journal of translational research · 2021Article
- Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models.Scientific reports · 2020Article
- Matrix Metalloproteinases in Acute Intracerebral Hemorrhage.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2020Review
- Common Data Elements for Subarachnoid Hemorrhage and Unruptured Intracranial Aneurysms: Recommendations from the Working Group on Subject Characteristics.Neurocritical care · 2019Article
- Common Data Elements for Unruptured Intracranial Aneurysms and Subarachnoid Hemorrhage Clinical Research: A National Institute for Neurological Disorders and Stroke and National Library of Medicine Project.Neurocritical care · 2019Article
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10 authors.
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Abstract
backgroundRapid 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.
methodsWe 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.
resultsWe 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.
conclusionsMultiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.
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