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

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Abstract

Hemorrhagic transformation (HT) and its associated neurological deterioration remain major concerns that limit decision-making in thrombolytic treatment for acute ischemic stroke (AIS). Several existing scales based on stroke severity, laboratory indicators, and cranial imaging characteristics are used to estimate the risk of HT. In recent years, machine learning techniques applied in radiomics analysis have been widely used to develop clinical decision-support tools. The present study applied radiomics analysis of pre-thrombolysis cranial non-contrast CT (NCCT) to predict HT after intravenous thrombolysis. Among 1255 cases of anterior-circulation AIS received intravenous recombinant tissue plasminogen activator (rtPA) thrombolysis, 132 patients developed HT on the cranial NCCT scan performed within 36 h post-thrombolysis. Using propensity score matching, a control group of 132 patients without HT, matched for age, gender, baseline systolic blood pressure and onset-to-treatment time, was selected. After excluding 10 patients with unsatisfactory image quality, 254 patients were finally enrolled in the radiomics analysis. They were randomly divided into training cohort and external validation cohort at a ratio of 4:1. Radiomic models consisting of six machine learning (ML) including C-Support Vector Classification (CSVC), Nu-Support Vector Classification (Nu-svc), Adaptive Boosting (AdaBoost), Xtreme Gradient Boosting (Xgboost), logical regression (LR) and random forest were constructed after extracting and selecting optimal features from the training cohort. Model performance was evaluated and compared in the validation cohort using receiver operating characteristic curves and the area under the curve (AUC). A total of 1874 radiomic features were extracted from the region of interest. The t-test and Least Absolute Shrinkage and Selection Operator identified 26 most relevant features. Among the above six ML classifiers, LR model demonstrated strong performance in both the cross-validation and validation cohorts. In five-fold cross-validation, the model achieved an AUC of 0.832, with a sensitivity of 0.76 and a specificity of 0.781 in the training cohort. In the external validation cohort, the AUC was 0.814, with a sensitivity of 0.68 and a specificity of 0.846. 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.

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