Comprehensive predictive modeling in subarachnoid hemorrhage: integrating radiomics and clinical variables.

ABSTRACT: Subarachnoid hemorrhage (SAH) is a severe condition with high morbidity and long-term neurological consequences. Radiomics, by extracting quantitative features from Computed Tomograhpy (CT) scans, may reveal imaging biomarkers predictive of outcomes. This study evaluates the predictive value of radiomics in SAH for multiple outcomes and compares its performance to models based on clinical data.Radiomic features were extracted from admission CTs using segmentations of brain tissue (white and gray matter) and hemorrhage. Machine learning models with cross-validation were trained using clinical data, radiomics, or both, to predict 6-month mortality, Glasgow Outcome Scale (GOS), vasospasm, and long-term hydrocephalus. SHapley Additive exPlanations (SHAP) analysis was used to interpret feature

SUBMITTER: Urbanos G 

PROVIDER: S-EPMC12187877 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature