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 external validation (n=196). Deep learning automatically segmented hematomas, and 1,690 radiomics features were extracted from admission non-contrast CT (NCCT). After reproducibility assessment, univariate screening, and LASSO selection, 42 features were integrated into a Rad-score. The Rad-score was combined with demographic, clinical, and conventional CT variables to construct two complementary machine learning models for functional outcome prediction and comprehensive prognostic stratification. Performance was assessed using R2, mean squared error (MSE), area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis, and external validation. Both models were internally and externally validated. Ambient temperature variation at symptom onset was explored as a potential prognostic modifier. Results: Forty-two reproducible radiomics features constituted the Rad-score. Adding radiomics substantially improved performance over conventional clinical and imaging models. For functional outcome prediction, the integrated model increased R2 from 0.35 to 0.93 and reduced MSE from 387 to 61. For prognostic risk stratification, the clinical-only model achieved an AUC of 0.59, whereas the fully integrated model achieved 0.95 and maintained robust external performance (AUC=0.86). Conclusions: We developed and validated a machine learning-based clinical decision support system integrating conventional CT radiomics and clinical variables for prognostic prediction and risk stratification in ICH. By capturing quantitative imaging information beyond conventional CT markers, this framework improved prognostic discrimination and generalizability. These findings support integrating quantitative imaging biomarkers into clinical decision-making for spontaneous ICH. The association between ambient temperature variation and disease progression warrants prospective investigation. Further prospective validation is warranted.