Background. Accurate prediction of ischemic lesion volume (ILV) in the subacute phase is essential to estimate functional outcome, as the two are positively associated. Ischemic lesions can continue to evolve between 24 hours and 1 week after stroke onset, even after successful treatment. Radiomics offers a promising approach for ILV prediction using non-contrast computed tomography (NCCT), the first-line imaging modality in AIS. However, applying CT radiomics in AIS remains challenging, as ischemic lesion segmentation is time consuming and challenging, due to its low contrast. 
Objective. This study aims to investigate whether radiomic features extracted from post-treatment NCCT scans, acquired at 24 hours after stroke onset, can be used to predict the subacute ischemic lesion volume at 1 week. In addition, it explores whether simplified annotations are feasible for radiomic feature extraction. As a secondary analysis, this study explores whether incorporating clinical data has an added value for this prediction task. 
Methods. Patients from the MR CLEAN-NOIV trial, with 24-hour and 1-week follow-up NCCT scans available, were included. The included patients were randomly divided into a pre-training set (80%) and a test set (20%). Radiomic features were extracted from the 24-hour NCCT scan using three annotation types: (1) the original segmentation, (2) a bounding box annotation, and (3) a circle annotation. Feature selection included reproducibility filtering, low-variance filtering, correlation-based clustering, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Three XGBoost radiomics regression models were trained, using five-fold cross-validation. Additionally, three combined models, using a combination of clinical and radiomic features, and two clinical models, using only clinical features, were constructed. The performance of the models was evaluated on the test set using the coefficient of determination (R²), concordance correlation coefficient (CCC), mean absolute error (MAE), and root mean squared error (RMSE). Feature importance was assessed using SHapley Additive exPlanations (SHAP). 
Results. The radiomics model based on the original segmentation achieved a high predictive performance (R² = 0.89, CCC = 0.95, MAE = 24 mL, RMSE = 31 mL). The radiomics model based on the bounding box achieved comparable performance, and the model based on the circle annotation yielded significantly lower performance. Incorporating clinical features did not significantly improve the predictive performance of the radiomics models. Across all well-performing models including radiomic features, the Run Length Non-Uniformity radiomic feature was a strong predictor of the 1-week ILV. 
Conclusion. Radiomic features extracted from 24-hour NCCT scans can accurately predict the subacute ILV at 1-week. A simplified bounding box annotation is a simpler and effective alternative to the detailed lesion segmentation, whereas the circle annotation showed poor performance and is not a good alternative for radiomic feature extraction in this context. These findings demonstrate the potential of radiomics and the use of simplified annotations for feature extraction to predict patient prognosis and guide personalized stroke care. However, further research is required before these models can be considered for clinical use.