Complex and continuous change in hypothetic risk of rupture of intracranial cerebral aneurysms – Bleb mandala –

Evaluation of predictive models of aneurysm focal growth and bleb development using machine learning techniques

BACKGROUND

The presence of blebs increases the rupture risk of intracranial aneurysms (IAs).

OBJECTIVE

To evaluate whether cross-sectional bleb formation models can identify aneurysms with focalized enlargement in longitudinal series.

METHODS

Hemodynamic, geometric, and anatomical variables derived from computational fluid dynamics models of 2265 IAs from a cross-sectional dataset were used to train machine learning (ML) models for bleb development. ML algorithms, including logistic regression, random forest, bagging method, support vector machine, and K-nearest neighbors, were validated using an independent cross-sectional dataset of 266 IAs. The models' ability to identify aneurysms with focalized enlargement was evaluated using a separate longitudinal dataset of 174 IAs. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), the sensitivity and specificity, positive predictive value, negative predictive value, F1 score, balanced accuracy, and misclassification error.

RESULTS

The final model, with three hemodynamic and four geometrical variables, along with aneurysm location and morphology, identified strong inflow jets, non-uniform wall shear stress with high peaks, larger sizes, and elongated shapes as indicators of a higher risk of focal growth over time. The logistic regression model demonstrated the best performance on the longitudinal series, achieving an AUC of 0.9, sensitivity of 85%, specificity of 75%, balanced accuracy of 80%, and a misclassification error of 21%.

CONCLUSIONS

Models trained with cross-sectional data can identify aneurysms prone to future focalized growth with good accuracy. These models could potentially be used as early indicators of future risk in clinical practice.

Differences Between Ruptured Aneurysms With and Without Blebs: Mechanistic Implications

PURPOSE

Blebs are known risk factors for intracranial aneurysm (IA) rupture. We analyzed differences between IAs that ruptured with blebs and those that ruptured without developing blebs to identify distinguishing characteristics among them and suggest possible mechanistic implications.

METHODS

Using image-based models, 25 hemodynamic and geometric parameters were compared between ruptured IAs with and without blebs (n = 673), stratified by location. Hemodynamic and geometric differences between bifurcation and sidewall aneurysms and for aneurysms at five locations were also analyzed.

RESULTS

Ruptured aneurysms harboring blebs were exposed to higher flow conditions than aneurysms that ruptured without developing blebs, and this was consistent across locations. Bifurcation aneurysms were exposed to higher flow conditions than sidewall aneurysms. They had larger maximum wall shear stress (WSS), more concentrated WSS distribution, and larger numbers of critical points than sidewall aneurysms. Additionally, bifurcation aneurysms were larger, more elongated, and had more distorted shapes than sidewall aneurysms. Aneurysm morphology was associated with aneurysm location (p < 0.01). Flow conditions were different between aneurysm locations.

CONCLUSION

Aneurysms at different locations are likely to develop into varying morphologies and thus be exposed to diverse flow conditions that may predispose them to follow distinct pathways towards rupture with or without bleb development. This could explain the diverse rupture rates and bleb presence in aneurysms at different locations.