ArticleEuropean radiology2021
A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- A multimodal prediction model for brain hematoma expansion based on deep neural network with transformer-driven collaborative attention fusion.Medical & biological engineering & computing · 2026Article
- Review
- Perilesional radiomics enhances 90-day outcome prediction in intracerebral hemorrhage: development and validation of a combined model with hematoma radiomics and clinical features.BMC medical imaging · 2026Article
- A novel dose-dependent predictor for hematoma expansion on non-contrast CT: the number of black hole signs in spontaneous intracerebral hemorrhage.Neurosurgical review · 2025Article
- Article
- Standard b-value DWI-derived stiffness index analysis may provide a way to evaluate the development of intracerebral hematoma.Frontiers in neurology · 2024Article
- Morphological characteristics of CT blend sign predict hematoma expansion and outcomes in intracerebral hemorrhage in elderly patients.Frontiers in medicine · 2024Article
- Review
- A convenient scoring system to distinguish intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis based on magnetic resonance imaging features.European radiology · 2023Article
- Prior knowledge-based precise diagnosis of blend sign from head computed tomography.Frontiers in neuroscience · 2023Article
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5 authors.
Funding
Abstract
objectiveTo predict early intracerebral haemorrhage expansion (HE) by comprehensive evaluation of commonly used noncontrast computed tomography (NCCT) features.
methodsTwo hundred eighty-eight patients who had a spontaneous intracerebral haemorrhage (ICH) were included. All of the patients had undergone baseline NCCT within 6 h after ICH symptom onset. Ten NCCT features were extracted. Univariate analysis and multivariable logistic regression analysis were used to select the features. Using the finally selected features, a logistic regression model was built with a training cohort (n = 202) and subsequently validated in an independent test cohort (n = 86). Additionally, stratification analysis was performed in cases with and without anticoagulant therapy.
resultsHE was found in 78 patients (27.1%). The blend sign and black hole sign were finally selected. The logistic regression model built with the two features exhibited accuracies of 76.7% and 75.6%, specificities of 98.6% and 98.4%, and positive predictive values (PPVs) of 83.3% and 75.0% for the training and test cohorts, respectively. The model also showed specificities of 100% and 98.5% and PPVs of 100% and 76.9% for the anticoagulant and non-anticoagulant drug use groups, respectively. These performances were better than those of each of the separate features.
conclusionsBy comprehensive evaluation, the model comprising the blend sign and black hole sign showed good performance for predicting early intracerebral haemorrhage expansion, particularly for high specificity and PPV, regardless of the anticoagulant status. KEY POINTS: • Early identification of patients who are more likely to have haematoma expansion is important for therapeutic intervention. • Many radiological features have been reported to correlate with intracerebral haemorrhage expansion. • By integrating only the blend sign and black hole sign, the logistic regression model showed good performance for predicting early intracerebral haemorrhage expansion.
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