Abstract Body: Introduction: Intracranial aneurysms (IAs) are weak outpouchings on cerebral vessels that can rupture, causing subarachnoid hemorrhage. Timely and accurate risk stratification of IAs is paramount.

Objective: Aneurysm wall enhancement (AWE) is a potential imaging biomarker for risk stratification. We propose to combine this with whole blood RNA sequencing to improve IA risk stratification.

Methods: We retrospectively collected images and blood of patients who had undergone vessel wall imaging. We performed RNA sequencing and radiomics analysis on the IA sacs on pre- and post-contrast T1 MRI scans. Using univariate analysis, we selected significantly different radiomic features (RFs). We removed all collinear features, and pooled radiomic and expression features. We classified each IA as symptomatic or asymptomatic based on symptoms at presentation. We then built separate machine learning models: one based solely on the radiomics features and another with combined features. We performed principal component analysis (PCA) and a leave-one-out (LOO) cross validation to quantify models’ performances.

Results: Our final cohort consisted of 7 patients. We found 34 significantly different RFs between symptomatic and asymptomatic IAs. The final feature set consisted of 9 RFs and 6 genes. PCA of whole dataset using the RFs alone reflected variances of 38% and 29% on the best principal components. We trained a random forest model using LOO cross validation and observed an accuracy of 57.1% (33.3% sensitivity, 75% specificity). With the addition of the gene expression features, we found the PCA explained variance to be 41% and 26%. The LOO accuracy improved to 85.7% (66.6% sensitivity, 100% specificity).

Conclusion: In this study, we demonstrated that radiomics from the aneurysm wall alone have a low predictive value in identifying symptomatic IAs. However, adding gene expression levels improves the predictive value. Future work will be aimed at adding more cases.