Three-dimensional aneurysm wall enhancement in fusiform intracranial aneurysms is associated with aneurysmal symptoms - Chen, Xuge; Peng, Fei; Liu, Xinmin; Xia, Jiaxiang; Niu, Hao; He, Xiaoxin; Xu, Boya; Bai, Xiaoyan; Li, Zhiye; Xu, Peng; Duan, Yonghong; Sui, Binbin; et al. (Frontiers in Neuroscience, 2023)
Background and purpose Aneurysm wall enhancement (AWE) in high-resolution magnetic resonance imaging (HR-MRI) is a potential biomarker for evaluating unstable aneurysms. Fusiform intracranial aneurysms (FIAs) frequently have a complex and curved structure. We aimed to develop a new three-dimensional (3D) aneurysmal wall enhancement (AWE) characterization method to enable comprehensive FIA evaluation and to investigate the ability of 3D-AWE to predict symptomatic FIA. Methods We prospectively recruited patients with unruptured FIAs and received 3T HR-MRI imaging from September 2017 to January 2019. 3D models of aneurysms and parent arteries were generated. Boundaries of the FIA were determined using 3D vessel diameter measurements. Dmax was the greatest diameter in the cross-section, while Lmax was the length of the centerline of the aneurysm. Signal intensity of the FIA was normalized to the pituitary stalk and then mapped onto the 3D model, then the average enhancement (3D-AWEavg), maximum enhancement (3D-AWEmax), enhancement area (AWEarea), and enhancement ratio (AWEratio) were calculated as AWE indicators, and the surface area of the entire aneurysm (Aarea) was also calculated. Areas with high AWE were defined as those with a value >0.9 times the signal intensity of the pituitary stalk. Multivariable logistic regression analyses were performed to determine independent predictors of aneurysm-related symptoms. FIA subtypes were defined as fusiform, dolichoectasia, and transitional. Differences between the three FIA subtypes were also examined. Results Forty-seven patients with 47 FIAs were included. Mean patient age was 55±12.62 years and 74.5% were male. Twenty-nine patients (38.3%) were symptomatic. After adjusting for baseline differences in age, hypertension, Lmax, and FIA subtype, the multivariate logistics regression models showed that 3D-AWEavg (odds ratio [OR], 4.029; P=0.019), 3D-AWEmax (OR, 3.437; P=0.022), AWEarea (OR, 1.019; P=0.008), and AWEratio (OR, 2.490; P=0.045) were independent predictors of aneurysm-related symptoms. Dmax and Aarea were larger and 3D-AWEavg, 3D-AWEmax, AWEarea, and AWEratio were higher with the transitional subtype than the other two subtypes. Conclusion The new 3D AWE method, which enables the use of numerous new metrics, can predict symptomatic FIAs. Different 3D-AWE between the three FIA subtypes may be helpful in understanding the pathophysiology of FIAs.