Predictive models for assessing the risk of brain aneurysm rupture

Increased contrast enhancement of the aneurysm wall may indicate aneurysm instability. The authors tested different predictive models of aneurysm instability on a cohort of patients imaged with high-resolution magnetic resonance imaging (HR-MRI).

Patients with intracranial aneurysms were prospectively scanned with HR-MRI. Aneurysm instability was defined as rupture or symptomatic status at presentation. Magnetic resonance images were analyzed, and 3D enhancement maps were generated to analyze aneurysm wall enhancement. Additionally, radiomics features were extracted from the aneurysm wall. Four different predictive models combining clinical patient information, morphological aneurysm metrics, wall enhancement, and radiomics data were created to compare their performance in predicting symptomatic aneurysm presentation.

A total of 129 intracranial aneurysms were included, with 34 (26%) being ruptured or symptomatic at presentation. The clinical model utilizing clinical variables and based on the Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid hemorrhage of another aneurysm, and Site of aneurysm (PHASES) score achieved an area under the curve (AUC) of 0.62 (70% accuracy, 44% sensitivity, and 79% specificity) in detecting symptomatic aneurysms. The combined model with the PHASES score and morphological aneurysm information (size ratio) improved the AUC to 0.79 (73% accuracy, 77% sensitivity, and 72% specificity). Adding aneurysm wall enhancement metrics further enhanced the model's performance, raising the AUC to 0.82 (81% accuracy, 65% sensitivity, and 86% specificity). The best performing model achieved an AUC to 0.87 (76% accuracy, 88% sensitivity, and 72% specificity) and included age and radiomic data.

The triage process for aneurysms is highly personalized and can benefit from incorporating clinical data, detailed morphological metrics of the aneurysm, and sophisticated analyses of aneurysm wall enhancement such as radiomics.