Keywords: Stroke, Stroke, Radiomics
Motivation: Early identification of hemorrhagic transformation (HT) is crucial for optimizing treatment in patients with acute ischemic stroke.
Goal(s): We aimed to develop and validate a multiomics model integrating radiomics features from multiparametric MRI with clinical data to improve HT prediction.
Approach: The multiomics model was developed and validated by combining radiomics features from diffusion and perfusion MRI with clinical variables derived from established clinical scoring systems.
Results: The radiomics model performed comparably to clinical models. The multiomics model demonstrated improved HT prediction compared to individual radiomics or clinical models in both internal and external validation sets.
Impact: The multiomics model, integrating clinical and radiomics features, improves the prediction of HT. These models could assist clinicians in making more informed decisions for managing patients with acute ischemic stroke.
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