Radiomics model for risk stratification of intracranial aneurysm: a high-resolution vessel wall imaging-based study

BACKGROUND: High-resolution vessel wall imaging (HR-VWI) enables in vivo assessment of aneurysm wall pathology, but conventional evaluation remains largely qualitative. This study aimed to develop and validate an HR-VWI-based radiomics model using aneurysm wall and parent artery wall features to identify symptomatic intracranial aneurysms (SIAs) and improve risk stratification. METHODS: A total of 410 patients with 446 intracranial aneurysms (IAs), comprising symptomatic (n = 112) and asymptomatic (n = 334) intracranial aneurysms from two centers, were included in this retrospective study. HR-VWI images were preprocessed to extract regions of interest from both the aneurysm wall and the parent artery (PA). Radiomic features were subsequently extracted using Pyradiomics, yielding a comprehensive set of 851 features per ROI. Feature selection was performed through a multi-stage process involving variance analysis, independent t-tests, and ElasticNet regularization. Based on these selected features, three imaging models were developed, including Radscore_IA (for the IA), Radscore_PA (for the PA), and Radscore_IA_PA (a combined model). Moreover, aneurysm location was incorporated as a morphological parameter into the refined models: Radscore_LOC_IA, Radscore_LOC_PA, and Radscore_LOC_IA_PA. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with the PHASES score serving as a comparative benchmark. RESULTS: Radiomics features derived from the IA (n = 7) and PA (n = 3) associated with SIAs were identified. In the validation cohort, the AUC values for SIA identification were as follows: PHASES (0.679), Radscore_IA (0.837), Radscore_PA (0.820), Radscore_IA_PA (0.878), Radscore_LOC_IA (0.852), Radscore_LOC_PA (0.842), and Radscore_LOC_IA_PA (0.888). The Radscore_LOC_IA_PA model exhibited the best performance, outperforming other models. Calibration and decision curve analyses confirmed the robustness and clinical application of all developed models. CONCLUSIONS: This study presents an innovative HR-VWI radiomics-based model for identifying high-risk SIA, Radscore_LOC_IA_PA, which integrates radiomics features from the IA wall and PA wall along with aneurysm location. Compared to traditional stratification methods, the model exhibits showed improved discrimination to identify high-risk SIA, enabling more accurate risk stratification and clinical management strategies for this patient population.