Machine learning–based radiomic analysis of T2-FLAIR white matter hyperintensity predicts hemorrhagic transformation after endovascular thrombectomy for acute ischemic stroke

Hemorrhagic transformation (HT) remains a major complication after endovascular therapy (EVT) in patients with acute ischemic stroke (AIS). This study aimed to develop and validate a machine-learning model based on T2-FLAIR white matter hyperintensity (WMH) radiomics combined with clinical variables to predict HT after EVT. In this dual-center retrospective study, 446 patients (mean age 61.88 ± 10.98 years; 332 [74.4%] male) with AIS who underwent EVT were enrolled from two centers. Patients from the primary center were randomly assigned to a training cohort and an internal test cohort, and patients from the second center served as an independent external validation cohort. Clinical risk factors were identified by univariate and multivariate logistic regression. Preoperative T2-FLAIR images were used to segment WMH lesions and extract radiomics features. After feature selection, three predictive models were then developed: a clinical model based on the independent clinical predictors, a radiomics model based on the radiomics signature, and a combined model integrating both. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration analysis, DeLong testing, and decision curve analysis. Among the 446 patients, 153 (34.3%) developed HT within 24–48 h after EVT. Multivariate analysis identified NIHSS score, occlusion site, and atrial fibrillation as independent clinical predictors of HT. In the internal test cohort, the combined model achieved the best discriminative performance (AUC = 0.818, 95% CI: 0.651–0.986), outperforming the clinical model (AUC = 0.740, 95% CI: 0.562–0.917) and the radiomics model (AUC = 0.806, 95% CI: 0.621–0.990). In the external validation cohort, the combined model maintained robust performance (AUC = 0.817, 95% CI: 0.746–0.889), which was comparable to the clinical model (AUC = 0.818, 95% CI: 0.742–0.894) and higher than the radiomics model alone (AUC = 0.764, 95% CI: 0.682–0.845). A combined model incorporating T2-FLAIR WMH radiomics and key clinical variables provides a feasible tool for individualized prediction of HT after EVT in patients with AIS, although its incremental discriminatory value over the clinical model alone was limited in the external validation cohort. This approach may help improve perioperative risk stratification and support personalized clinical decision-making.