Predicting Functional Outcomes of Endovascular Thrombectomy in Acute Ischemic Stroke Using a Clinical-Radiomics Nomogram.

Background: Endovascular thrombectomy (EVT) is recommended for acute ischemic stroke (AIS) due to large-vessel occlusion. However, approximately 50% of patients still face poor outcomes post-procedure. This study aimed to assess whether a nomogram model that integrates CT angiography radiomics features and clinical variables can predict EVT outcomes in AIS patients.

Methods: 159 EVT patients were randomly divided into training and validation groups at a 7:3 ratio. A modified Rankin Scale (mRS) ≤ 2 at 90 days indicated a favorable outcome. We used univariate and multivariate logistic regression to identify analytic and radiomic predictors and create predictive models. Model performance was evaluated using the AUC, Hosmer-Lemeshow test, and decision curve analysis for discrimination, calibration, and clinical utility.

Results: A 19-feature radiomic signature reached an AUC of 0.79. Combining it with age, baseline NIHSS, diabetes, and statin use raised the clinical-radiomics nomogram's AUC to 0.85. Both decision curve and calibration curve analyses showed strong performance.

Conclusion: Combining radiomics nomogram with clinical predictors could effectively forecast EVT outcomes in acute anterior circulation large vessel occlusion stroke patients.