High-resolution Vessel Wall Imaging-driven Radiomic Analysis for the Precision Prediction of Intracranial Aneurysm Rupture Risk: A Promising Approach

Wenqing Yuan^1,2Shuangyan Jiang^1,2Zihang Wang^1,2Chang Yan^1,2Yongxiang Jiang³Dajing Guo^1*Ting Chen^1*

• ¹Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University., No.74 Linjiang Rd, Yuzhong District, 400010, Chongqing, China
• ²Chongqing Medical University, Chongqing, China
• ³Department of Neurosurgery, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China

This study aimed to extract the radiomic features of intracranial aneurysm (IA) and parent artery (PA) walls from high-resolution vessel wall imaging (HR-VWI) images and construct and validate machine learning (ML) predictive models by comparing them with the radiomics score (Rad-score). Methods: In this study, 356 IAs from 306 patients were retrospectively analyzed at Yuzhong Center and randomly divided into training and test cohorts in an 8:2 ratio. Additionally, 66 IAs from 58 patients were used at Jiangnan Center to validate the predictive model. Radiomic features of the IA and PA walls were extracted from the contrast-enhanced HR-VWI images. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the training cohort features to identify optimal rupture-associated features. The Rad-score model was constructed by calculating the total score derived from the weighted sum of optimal radiomic features, and three ML models were built using the XGBoost, LightGBM, and CART algorithms, and evaluated using both the test and external validation cohorts. Results: Eight optimal IA wall features and four PA wall features were identified. The Rad-score model demonstrated an area under the curve (AUC) of 0.858, 0.800, and 0.770 for the training, test, and external validation cohorts, respectively. Among the three ML models, the XGBoost model performed best across all cohorts, with AUC values of 0.983, 0.891, and 0.864, respectively. Compared to the Rad-score model, the XGBoost model exhibited superior AUC values (p < 0.05), better calibration curve Brier scores, and greater net clinical benefit. Conclusion: The radiomic features extracted from HR-VWI images demonstrated robust predictive utility for IA rupture risk in both the Rad-score and ML models. The XGBoost-based ML model outperformed the Rad-score model in efficacy and performance, and proved to be a noninvasive, efficient, and accurate tool for identifying high-risk IA patients.