Predicting incomplete occlusion of intracranial aneurysms treated with flow diverters using machine learning models.

Abstract

Intracranial saccular aneurysms are vascular malformations responsible for 80% of nontraumatic brain hemorrhage. Recently, flow diverters have been used as a less invasive therapeutic alternative for surgery. However, they fail to achieve complete occlusion after 6 months in 25% of cases. In this study, the authors built a tool, using machine learning (ML), to predict the aneurysm occlusion outcome 6 months after treatment with flow diverters. A total of 667 aneurysms in 616 patients treated with the Pipeline embolization device at a tertiary referral center between January 2011 and December 2017 were included. To build the predictive tool, two experiments were conducted. In the first experiment, six ML algorithms (support vector machine [SVM], decision tree, random forest [RF], k-nearest neighbor, XGBoost, and CatBoost) were trained using 26 features related to patient risk factors and aneurysm morphological characteristics, and the results were compared with logistic regression (LR) modeling. In the second experiment, the models were trained using the top 10 features extracted by Shapley additive explanation (SHAP) analysis performed on the RF model. The results showed that the authors' tool can better predict the occlusion outcome than LR (accuracy of 89% for the SVM model vs 62% for the LR model), even when trained using a subset of the features (83% accuracy). SHAP analysis revealed that age, hypertension, smoking status, branch vessel involvement, aneurysm neck, and larger diameter dimensions were among the most important features contributing to accurate predictions. In this study, an ML-based tool was developed that successfully predicts outcome in intracranial aneurysms treated with flow diversion, thus helping neurosurgeons to practice a more refined approach and patient-tailored medicine.