Prediction and SHAP Analysis Integrating Morphological and Hemodynamic Parameters for Unruptured Intracranial...

Although most unruptured intracranial aneurysms (UIAs) have good prognosis after flow diverter (FD) treatment, some remain unoccluded for extended periods, posing a persistent rupture risk. This study aims to develop a predictive model for UIA occlusion after FD treatment through integrating morphological and hemodynamic parameters, which may be critical for personalized postoperative management.

Data from patients with single UIAs treated with stand-alone FD were collected from June 2018 to December 2022 in four cerebrovascular disease centers. Morphological parameters were obtained from 3D reconstructed aneurysm models, and hemodynamic parameters were derived by computational fluid dynamics (CFD) analysis. A predictive model for aneurysm occlusion was constructed using various machine learning algorithms, including logistic regression, Random Forest, XGBoost, and K-Nearest Neighbors. Model performances were evaluated through repeated cross-validation, 0.632 bootstrap, and 0.632+ bootstrap. Shapley additive explanation (SHAP) analysis was employed to assess the contribution of each parameter to UIA occlusion.

Seventy-nine patients were reviewed; a total of 51 cases met the criteria, with an average age of 53.9 ± 9.9 years. The average aneurysm diameter was 3.72 ± 2.72 mm, comprising 29 occlusions and 22 non-occlusions. Five variables were selected for further modeling, including follow-up time > 6 months, aneurysm rupture ratio (ArR), occlusion ratio (OsR), parent artery wall shear stress (WSS), and the change of parent artery WSS. Logistic regression outperformed other algorithms, achieving an area under the curve (AUC) above 0.75, indicating good predictive performance. SHAP analysis revealed that the change of parent artery WSS contributed most significantly to accurate and early prediction. Additionally, a web application software was developed to assist clinicians in real-time aneurysm occlusion prediction.

This study developed a robust predictive model for UIA occlusion following FD treatment by integrating morphological and hemodynamic parameters, which may provide potentially valuable decision-making support for optimizing treatment strategies.