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

Date Published: 2026-08-19

Abstract:
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. 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. 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. The combined CT-based radiomics model effectively predicts complete recanalization, providing an objective tool for patient selection and treatment optimization.

Introduction:
Endovascular thrombectomy (EVT) is the cornerstone treatment for acute ischemic stroke (AIS) due to anterior circulation large-vessel occlusion ( 1 ). Successful recanalization is crucial for improving patient prognosis; however, recanalization rates vary significantly across studies ( 2–4 ). When EVT fails, repeated endovascular manipulations and contrast agent exposure may impair outcomes ( 5 ). Therefore, preoperative prediction of recanalization outcomes to identify optimal candidates represents a critical…

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