Topographical Analysis of Aneurysm Wall Enhancement With 3-Dimensional Mapping

Validation of a Mathematical Model for Rupture Status of Spherical Intracranial Aneurysms

PURPOSE

An accurate mathematical model of intracranial aneurysm (IA) mechanics is of great value for its potential utility in assessing probability of IA rupture. Such a model for spherical IAs has been developed which predicts a wall-thickness-to-IA-radius ratio (WTR) of 6.1 × 10-3 at which IAs rupture. To our knowledge, no further work has been done to validate this model with clinical data. We aim to assess the accuracy and utility of this model of spherical IA rupture mechanics.

METHODS

Aneurysm height, width, neck diameter, and vessel radius were measured on radiologic images of IAs of the basilar terminus, anterior communicating, and posterior communicating arteries. Geometric modeling was used to approximate IA wall thickness. Calculations were performed with and without accounting for changes in IA morphology which have been shown to occur post-rupture. Receiver operating characteristic (ROC) curves and positive likelihood ratios (LR) were produced for WTR, aspect ratio (AR), bottleneck factor (BF), and size ratio (SR). Logistic regression analysis was performed to determine the WTR where there is a 50% chance of presentation as a ruptured aneurysm in our cohort.

RESULTS

52 unruptured and 28 ruptured spherical IAs were included. ROC curve analysis revealed similar areas under the curve for WTR, AR, BF, and SR, ranging from 0.636 to 0.773 with overlapping confidence intervals. LRs ranged from 1.34 (95% CI 1.09-1.65) for AR calculated with post-rupture dimensional adjustments to 2.14 (95% CI 1.45-3.14) for WTR and BF calculated without post-rupture adjustments. Logistic regression revealed a strong association between decreased WTR and rupture status. The point at which there is a 50% chance of presentation as ruptured was found to be WTR = 7.9 × 10-3 when calculated without post-rupture adjustments and WTR' = 6.2 × 10-3 when calculated with post-rupture adjustments, from which the proposed 6.1 × 10-3 differs by 23% and 1.4%, respectively.

CONCLUSIONS

The model for IA rupture mechanics assessed in this study agrees reasonably well with clinical data and could serve as a foundation for further investigation. It additionally performs well in discriminating between ruptured and unruptured aneurysms, though its performance in this dataset is similar to more conventional, simpler parameters. Most importantly, this study demonstrates that biomathematical models can provide valuable insight into the nature of aneurysmal lesions despite their simplifying assumptions.

Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Aneurysm wall enhancement (AWE) has the potential to be used as an imaging biomarker for the risk stratification of intracranial aneurysms (IAs). Radiomics provides a refined approach to quantify and further characterize AWE's textural features. This study examines the performance of AWE quantification combined with clinical information in detecting symptomatic IAs. Ninety patients harboring 104 IAs (29 symptomatic and 75 asymptomatic) underwent high-resolution magnetic resonance imaging (HR-MRI). The assessment of AWE was performed using two different methods: 3D-AWE mapping and composite radiomics-based score (RadScore). The dataset was split into training and testing subsets. The testing set was used to build two different nomograms using each modality of AWE assessment combined with patients' clinical information and aneurysm morphological data. Finally, each nomogram was evaluated on an independent testing set. A total of 22 radiomic features were significantly different between symptomatic and asymptomatic IAs. The 3D-AWE mapping nomogram achieved an area under the curve (AUC) of 0.77 (63% accuracy, 78% sensitivity, and 58% specificity). The RadScore nomogram exhibited a better performance, achieving an AUC of 0.83 (77% accuracy, 89% sensitivity, and 73% specificity). The comprehensive analysis of IAs with the quantification of AWE data through radiomic analysis, patient clinical information, and morphological aneurysm metrics achieves a high accuracy in detecting symptomatic IA status.

Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Background

Aneurysm wall enhancement (AWE) has the potential to be used as an imaging biomarker for the risk stratification of intracranial aneurysms (IAs). Radiomics provides a refined approach to quantify and further characterize AWE's textural features. This study examines the performance of AWE quantification combined with clinical information in detecting symptomatic IAs.

Methods

Ninety patients harboring 104 IAs (29 symptomatic and 75 asymptomatic) underwent high-resolution magnetic resonance imaging (HR-MRI). The assessment of AWE was performed using two different methods: 3D-AWE mapping and composite radiomics-based score (RadScore). The dataset was split into training and testing subsets. The testing set was used to build two different nomograms using each modality of AWE assessment combined with patients' demographic information and aneurysm morphological data. Finally, each nomogram was evaluated on an independent testing set.

Results

A total of 22 radiomic features were significantly different between symptomatic and asymptomatic IAs. The 3D-AWE Mapping nomogram achieved an area under the curve (AUC) of 0.77 (63% accuracy, 78% sensitivity and 58% specificity). The RadScore nomogram exhibited a better performance, achieving an AUC of 0.83 (77% accuracy, 89% sensitivity and 73% specificity).

Conclusions

Combining AWE quantification through radiomic analysis with patient demographic data in a clinical nomogram achieved high accuracy in detecting symptomatic IAs.

Three-dimensional aneurysm wall enhancement in fusiform intracranial aneurysms is associated with aneurysmal symptoms

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 3 T 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. D_max was the greatest diameter in the cross-section, while L_max 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-AWE_avg), maximum enhancement (3D-AWE_max), enhancement area (AWE_area), and enhancement ratio (AWE_ratio) were calculated as AWE indicators, and the surface area of the entire aneurysm (A_area) 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, L_max, and FIA subtype, the multivariate logistics regression models showed that 3D-AWE_avg (odds ratio [OR], 4.029; p = 0.019), 3D-AWE_max (OR, 3.437; p = 0.022), AWE_area (OR, 1.019; p = 0.008), and AWE_ratio (OR, 2.490; p = 0.045) were independent predictors of aneurysm-related symptoms. D_max and A_area were larger and 3D-AWE_avg, 3D-AWE_max, AWE_area, and AWE_ratio 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.

Comprehensive morphomechanical analysis of brain aneurysms

OBJECTIVE

Brain aneurysms comprise different compartments that undergo unique biological processes. A detailed multimodal analysis incorporating 3D aneurysm wall enhancement (AWE), computational fluid dynamics (CFD), and finite element analysis (FEA) data can provide insights into the aneurysm wall biology.

METHODS

Unruptured aneurysms were prospectively imaged with 7 T high-resolution MRI (HR-MRI). 3D AWE color maps of the entire aneurysm wall were generated and co-registered with contour plots of morphomechanical parameters derived from CFD and FEA. A multimodal analysis of the entire aneurysm was performed using 3D circumferential AWE (3D-CAWE), wall tension (WT), time-averaged wall shear stress (TAWSS), wall shear stress gradient (WSSG), and oscillatory shear index (OSI). A detailed compartmental analysis of each aneurysm's dome, bleb, and neck was also performed.

RESULTS

Twenty-six aneurysms were analyzed. 3D-CAWE + aneurysms had higher WT (p = 0.03) and higher TAWSS (p = 0.045) than 3D-CAWE- aneurysms. WT, TAWSS, and WSSG were lower in areas of focal AWE in the aneurysm dome compared to the neck (p = 0.009, p = 0.049, and p = 0.040, respectively), whereas OSI was higher in areas of focal AWE compared to the neck (p = 0.020). When compared to areas of no AWE of the aneurysm sac (AWE = 0.92 vs. 0.49, p = 0.001), blebs exhibited lower WT (1.6 vs. 2.45, p = 0.010), lower TAWSS (2.6 vs. 6.34), lower OSI (0.0007 vs. 0.0010), and lower WSSG (2900 vs. 5306). Fusiform aneurysms had a higher 3D-CAWE and WT than saccular aneurysms (p = 0.046 and p = 0.003, respectively).

CONCLUSIONS

Areas of focal high AWE in the sac and blebs are associated with low wall tension, low wall shear stress, and low flow conditions (TAWSS and WSSG). Conversely, the neck had average AWE, high wall tension, high wall shear stress, and high flow conditions. The aneurysm dome and the aneurysm neck have different morphomechanical environments, with increased mechanical load at the neck.