ArticleAnnals of translational medicine2022
Radiomics for predicting revised hematoma expansion with the inclusion of intraventricular hemorrhage growth in patients with supratentorial spontaneous intraparenchymal hematomas.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- Efficacy of desferrioxamine mesylate in intracerebral hematoma: a systemic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Pooled it
- Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis.Frontiers in oncology · 2022Pooled it
- Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation.Frontiers in human neuroscience · 2026Review
- An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.Frontiers in neuroscience · 2026Article
- Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage.Scientific reports · 2025Article
- Harnessing radiomics to predict hematoma expansion in intracerebral hemorrhage: a step towards personalized care.European radiology · 2025Article
- Combining Non-Contrast CT Signs With Onset-to-Imaging Time to Predict the Evolution of Intracerebral Hemorrhage.Korean journal of radiology · 2024Article
- Review
- Non-Contrasted CT Radiomics for SAH Prognosis Prediction.Bioengineering (Basel, Switzerland) · 2023Article
- Andexanet Alfa for Reversal of Factor Xa Inhibitors in Intracranial Hemorrhage: Observational Cohort Study.Journal of clinical medicine · 2022Article
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrevious radiomics analyses of hematoma expansion have been based on the traditional definition, which only focused on changes in intraparenchymal volume. However, the ability of radiomics-related models to predict revised hematoma expansion (RHE) with the inclusion of intraventricular hemorrhage expansion remains unclear. To develop and validate a noncontrast computed tomography (NCCT)-based clinical- semantic-radiomics nomogram to identify supratentorial spontaneous intracerebral hemorrhage (sICH) patients with RHE on admission.
methodsIn this double-center retrospective study, data from 376 patients with sICH (training set: n=299; test set: n=77; external validation cohort: n=91) were reviewed. A radiomics model, a clinical-semantic model, and a combined model were then constructed based on the logistic regression machine learning approach. Radiomics features were extracted and selected by least absolute shrinkage and selection operator (LASSO) with 5-fold cross validation. Furthermore, the classical BRAIN scoring system was also constructed to predict RHE. Discriminative performance of the models was evaluated on the training and test set with area under the curve (AUC) and decision curve analysis (DCA).
resultsThe addition of radiomics to clinical-semantic factors significantly improved the prediction performance of RHE compared with the clinical-semantic model alone in the training (AUC, 0.94
conclusionsThe clinical-semantic-radiomics combined model had the greatest potential for discriminating RHE, and significantly outperformed the classical BRAIN scoring system.
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