ReviewFrontiers in neurology2024
The clinical potential of radiomics to predict hematoma expansion in spontaneous intracerebral hemorrhage: a narrative review.
Review in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence and machine learning driven segmentation and quantification models for brain arteriovenous malformations: A systematic review.Neuroradiology · 2025Pooled it
- Review
- Article
- Radiomic hemorrhage roundness predicts outcome beyond the ICH score in deep intracerebral hemorrhage with IVH.Journal of neurology · 2026Article
- Enhancing hematoma expansion prediction in hypertensive intracerebral hemorrhage based on habitat and perihematomal edema radiomics from non-contrast CT: a dual-center study.Frontiers in neuroscience · 2026Article
- Adapting foundation models for rapid clinical response: intracerebral hemorrhage segmentation in emergency settings.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spontaneous intracerebral hemorrhage (sICH) is associated with significant morbidity and mortality, with subsequent hematoma expansion (HE) linked to worse neurologic outcomes. Accurate, real-time predictions of the risk of HE could enable tailoring management-including blood pressure control or surgery-based on individual patient risk. Although multiple radiographic markers of HE have been proposed based on standard imaging, their clinical utility remains limited by a reliance on subjective interpretation of often ambiguous findings and a poor overall predictive power. Radiomics refers to the quantitative analysis of medical images that can be combined with machine-learning algorithms to identify predictive features for a chosen clinical outcome with a granularity beyond human limitations. Emerging data have supported the potential utility of radiomics in the prediction of HE after sICH. In this review, we discuss the current clinical management of sICH, the impact of HE and standard imaging predictors, and finally, the current data and potential future role of radiomics in HE prediction and management of patients with sICH.
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Registered trials
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