Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation

Abstract

Intracerebral hemorrhage (ICH) is associated with substantial morbidity and mortality, and early hematoma expansion (HE), defined as an increase in hematoma volume on follow-up imaging, is a major determinant of early neurological deterioration and poor outcomes. Accurate, hyperacute prediction of HE is critical for guiding precise interventions, such as intensive blood pressure management and surgical evacuation. This narrative review examines the progression of prediction models from univariate predictors to systems incorporating artificial intelligence (AI), with particular attention to risk factors, model validation, and clinical translation. We further examine the validation status of current prediction models and the methodological and practical barriers to their clinical implementation. We review the progression from established independent predictors, such as the CT angiography (CTA) spot sign (SpS) and non-contrast CT (NCCT) island sign (IS), to multivariable clinical scoring systems, radiomics-based machine learning (ML) approaches, and deep learning (DL) models, while also discussing emerging generative architectures. Furthermore, major clinical trials evaluating strategies to limit HE are reviewed. Finally, the current challenges and future prospects of predictive models of HE are also elucidated.