An automated, explainable, NCCT-based clinical decision-support system for spontaneous intracerebral hemorrhage.

Background: Spontaneous intracerebral hemorrhage (ICH) has high disability and mortality. Accurate early prognostication remains challenging in routine practice. Conventional CT assessment relies mainly on hematoma volume and rough anatomical location, limiting individualized prognosis. Radiomics combined with machine learning may improve prognostic assessment by extracting quantitative texture and heterogeneity features.Methods: In this retrospective multicenter study, we included 2,680 consecutive patients with spontaneous ICH: training (n=1,876), internal validation (n=804), and independent

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