CT-based intrathrombus and perithrombus radiomics for predicting complete recanalization after endovascular thrombectomy in acute ischemic stroke.

OBJECTIVE: To develop and validate CT-based radiomics models incorporating intrathrombus and perithrombus features for predicting complete recanalization [modified Thrombolysis in Cerebral Infarction (mTICI)2c/3] after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS), and to identify the optimal machine learning classifier. MATERIALS AND METHODS: This retrospective study included 406 AIS patients with anterior circulation large-vessel occlusion from three centers (December 2018-April 2024). Patients were allocated to training (n = 178), internal testing (n = 77), and external validation (n = 151) cohorts. Complete recanalization was defined as mTICI 2c/3. A total of 428 radiomics features were extracted from non-contrast CT and CT angiography (CTA). Least absolute shrinkage and selection operator (LASSO) regression and eleven classifiers were employed. RESULTS: The combined intrathrombus-perithrombus model with logistic regression achieved area under the curve (AUC) values of 0.93 (training), 0.88 (testing), and 0.86 (validation), outperforming single-region models. Decision curve analysis confirmed superior clinical utility. The perithrombus region contributed dominantly (10 of 15 features) to the combined model. CONCLUSION: The combined CT-based radiomics model effectively predicts complete recanalization, providing an objective tool for patient selection and treatment optimization.