Abstract
OBJECTIVES: This study attempted to determine potential predictors among radiomics features for poor prognosis in aneurysmal subarachnoid hemorrhage (aSAH), develop models for prediction, and verify their predictive power. METHODS: In total, 252 patients with aSAH were included in this study and categorized into favorable and poor outcome groups based on the modified Rankin Scale score 3 months after event. Radiomics features of the ruptured intracranial aneurysm extracted from computed tomography angiography images were selected using least absolute shrinkage and selection operator regression and 10-fold cross-validation. A radiomics score was created by selecting the optimal features. Other risk factors for a poor prognosis were screened using multivariate regression analysis. Three models (clinical, aneurysm, and clinical-aneurysm combined models) were developed. The performance of the models was assessed using receiver operating characteristic (ROC) curves. A clinical-aneurysm combined nomogram was constructed to forecast the risk of poor prognosis in patients with aSAH. RESULTS: A total of three clinical variables and six radiomics features were shown to have a significant association with poor prognosis in patients with aSAH. In the training cohort, the clinical, aneurysm, and clinical-aneurysm combined models had areas under the ROC curves of 0.846, 0.762, and 0.893, respectively. In the testing cohort, these models had areas under the ROC curves of 0.848, 0.753, and 0.869, respectively. CONCLUSION: The radiomics characteristics of ruptured intracranial aneurysms are valuable to predict prognosis after aSAH. The clinical-aneurysm combined model exhibited the best among the three models. The clinical-aneurysm combined nomogram is a reliable and effective tool for predicting poor prognosis in patients with aSAH.