Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models

In this study, the LR classifier demonstrated favorable predictive performance among the six models evaluated, showing high specificity in identifying HT based on radiomic features extracted from pre-treatment cranial NCCT in anterior-circulation AIS. These findings suggest potential for early risk stratification of patients at elevated risk for post-thrombolysis hemorrhagic transformation, which may contribute to more individualized treatment decisions pending further external validation.