Nonsphericity Index and Size Ratio Identify Morphologic Differences between Growing and Stable Aneurysms in a Longitudinal Study of 93 Cases

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.

Image-derived Metrics Quantifying Hemodynamic Instability Predicted Growth of Unruptured Intracranial Aneurysms

Background

While image-derived predictors of intracranial aneurysm (IA) rupture have been well-explored, current understanding of IA growth is limited. Pulsatility index (PI) and wall shear stress pulsatility index (WSSPI) are important metrics measuring temporal hemodynamic instability. However, they have not been investigated in IA growth research. The present study seeks to verify reliable predictors of IA growth with comparative analyses of several important morphological and hemodynamic metrics between stable and growing cases among a group of unruptured IAs.

Methods

Using 3D images, vascular models of 16 stable and 20 growing cases were constructed and verified using Geodesic techniques. With an overall mean follow-up period of 25 months, cases exhibiting a 10% or higher increase in diameter were considered growing. Patient-specific, pulsatile simulations were performed, and hemodynamic calculations were computed at 5 important regions of each aneurysm (inflow artery, aneurysm neck, body, dome, and outflow artery). Index values were compared between growing and stable IAs using ANCOVA controlling for aneurysm diameter. Stepwise multiple logistic regression and ROC analyses were conducted to investigate predictive models of IA growth.

Results

Compared to stable IAs, growing IAs exhibited significantly higher intrasaccular PI, intrasaccular WSSPI, intrasaccular spatial flow rate deviation, and intrasaccular spatial wall shear stress (WSS) deviation. Stepwise logistic regression analysis revealed a significant predictive model involving PI at aneurysm body, WSSPI at inflow artery, and WSSPI at aneurysm body.

Conclusions

Our results showed that high degree of hemodynamic variations within IAs is linked to growth, even after controlling for morphological parameters. Further, evaluation of PI in conjunction with WSSPI yielded a highly accurate predictive model of IA growth. Upon validation in future cohorts, these metrics may aid in early identification of IA growth and current understanding of IA remodeling mechanism.

Combination of Morphological and Hemodynamic Parameters for Assessing the Rupture Risk of Intracranial Aneurysms: a Retrospective Study On Mirror Middle Cerebral Artery Aneurysms

Discordant findings were frequently reported by studies dedicated to exploring the association of morphological/hemodynamic factors with the rupture of intracranial aneurysms (IAs), probably owing to insufficient control of confounding factors. In this study, we aimed to minimize the influences of confounding factors by focusing IAs of interest on mirror aneurysms and, meanwhile, modeling IAs together with the cerebral arterial network to improve the physiological fidelity of hemodynamic simulation. 52 mirror aneurysms located at the middle cerebral artery (MCA) in 26 patients were retrospectively investigated. Numerical tests performed on two randomly selected patients demonstrated that over truncation of cerebral arteries proximal to the MCA during image-based model reconstruction led to uncertain changes in computed values of intra-aneurysmal hemodynamic parameters, which justified the minimal truncation strategy adopted in our study. Five morphological parameters (i.e., volume (V), height (H), dome area (DA), non-sphericity index (NSI), and size ratio (SR)) and two hemodynamic parameters (i.e., peak WSS (peakWSS), and pressure loss coefficient (PLc)) were found to differ significantly between the ruptured and unruptured aneurysms and proved by receiver operating characteristic (ROC) analysis to have potential value for differentiating the rupture status of aneurysm with the areas under curve (AUCs) ranging from 0.681 to 0.763. Integrating V, SR, peakWSS and PLc or some of them into regression models considerably improved the classification of aneurysms, elevating AUC up to 0.864, which indicates that morphological and hemodynamic parameters have complementary roles in assessing the risk of aneurysm rupture.

The influence of aneurysm morphology on the volume of hemorrhage after rupture

OBJECTIVE

Factors determining the risk of rupture of intracranial aneurysms have been extensively studied; however, little attention is paid to variables influencing the volume of bleeding after rupture. In this study the authors aimed to evaluate the impact of aneurysm morphological variables on the amount of hemorrhage.

METHODS

This was a retrospective cohort analysis of a prospectively collected data set of 116 patients presenting at a single center with subarachnoid hemorrhage due to aneurysmal rupture. A volumetric assessment of the total hemorrhage volume was performed from the initial noncontrast CT. Aneurysms were segmented and reproduced from the initial CT angiography study, and morphology indexes were calculated with a computer-assisted approach. Clinical and demographic characteristics of the patients were included in the study. Factors influencing the volume of hemorrhage were explored with univariate correlations, multiple linear regression analysis, and graphical probabilistic modeling.

RESULTS

The univariate analysis demonstrated that several of the morphological variables but only the patient's age from the clinical-demographic variables correlated (p < 0.05) with the volume of bleeding. Nine morphological variables correlated positively (absolute height, perpendicular height, maximum width, sac surface area, sac volume, size ratio, bottleneck factor, neck-to-vessel ratio, and width-to-vessel ratio) and two correlated negatively (parent vessel average diameter and the aneurysm angle). After multivariate analysis, only the aneurysm size ratio (p < 0.001) and the patient's age (p = 0.023) remained statistically significant. The graphical probabilistic model confirmed the size ratio and the patient's age as the variables most related to the total hemorrhage volume.

CONCLUSIONS

A greater aneurysm size ratio and an older patient age are likely to entail a greater volume of bleeding after subarachnoid hemorrhage.

Definition and extraction of 2D shape indices of intracranial aneurysm necks for rupture risk assessment

Purpose

Intracranial aneurysms are local dilations of brain vessels. Their rupture, as well as their treatment, is associated with high risk of morbidity and mortality. In this work, we propose shape indices for aneurysm ostia for the rupture risk assessment of intracranial aneurysms.

Methods

We analyzed 84 middle cerebral artery bifurcation aneurysms (27 ruptured and 57 unruptured) and their ostia, with respect to their size and shape. We extracted 3D models of the aneurysms and vascular trees. A semi-automatic approach was used to separate the aneurysm from its parent vessel and to reconstruct the ostium. We used known indices to quantitatively describe the aneurysms. For the ostium, we present new shape indices: the 2D Undulation Index (UI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_\mathrm{2D}$$\end{document}2D), the 2D Ellipticity Index (EI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_\mathrm{2D}$$\end{document}2D) and the 2D Noncircularity Index (NCI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_\mathrm{2D}$$\end{document}2D). Results were analyzed using the Student t test, the Mann–Whitney U test and a correlation analysis between indices of the aneurysms and their ostia.

Results

Of the indices, none was significantly associated with rupture status. Most aneurysms have an NCI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_\mathrm{2D}$$\end{document}2D below 0.2. Of the aneurysms that have an NCI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_\mathrm{2D}$$\end{document}2D above 0.5, only one is ruptured, which indicates that ruptured aneurysms often have a circular-shaped ostium. Furthermore, the ostia of ruptured aneurysms tend to have a smaller area, which is also correlated with the aneurysm’s size. While also other variables were significantly correlated, strong linear correlations can only be seen between the area of the ostium with the aneurysm’s volume and surface.

Conclusion

The proposed shape indices open up new possibilities to quantitatively describe and compare ostia, which can be beneficial for rupture risk assessment and subsequent treatment decision. Additionally, this work shows that the ostium area and the size of the aneurysm are correlated. Further longitudinal studies are necessary to analyze whether stable and unstable aneurysms can be distinguished by their ostia.

Deep Shape Features for Predicting Future Intracranial Aneurysm Growth

Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support.

Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model.

Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively.

Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging.

Reliability and accuracy assessment of morphometric measurements obtained with software for three-dimensional reconstruction of brain aneurysms relative to cerebral angiography measures

OBJECTIVE

To analyze the reliability and accuracy of morphological measurements of software employed to three-dimensionally reconstruct aneurysms and vessels (VMTKlab, version 1.6.1,) with computed tomography angiography (CTA) as the source of images. Agreement with measurements from three-dimensional digital subtraction angiography (3 D-DSA) was evaluated.

METHODS

We evaluated 40 patients presenting with aneurysmal subarachnoid hemorrhage (aSAH). We analyzed four main variables of the aneurysm morphology: absolute height (size), neck (maximum neck width), perpendicular height, and maximum width. The CTA images were uploaded to the software and then segmented to reconstruct the aneurysm. This new method was compared to the current gold standard-3D reconstruction of pretreatment cerebral angiography. We used intraclass correlation coefficient (ICC) and Bland-Altman plot analyses to evaluate the agreement between these methods.

RESULTS

The ICCs obtained for absolute height, neck, perpendicular height, and maximum width were 0.85, 0.57, 0.85, and 0.89, respectively. This implied good agreement except for the neck of the aneurysm (moderate agreement). Bland-Altman plots are presented for the four indexes. The average of the differences was not significant in terms of absolute height, perpendicular height, and maximum width indicating good agreement. However, it was significant for the neck of the aneurysm.

CONCLUSIONS

We report good agreement between the values generated using VMTKlab and cerebral angiography for three of the four main variables. Discrepancies in neck diameter are not surprising and its underestimation with a traditional delineation from cerebral angiography has been reported before.

Association between aneurysm hemodynamics and wall enhancement on 3D vessel wall MRI

OBJECTIVE

Aneurysm wall enhancement (AWE) on 3D vessel wall MRI (VWMRI) has been suggested as an imaging biomarker for intracranial aneurysms (IAs) at higher risk of rupture. While computational fluid dynamics (CFD) studies have been used to investigate the association between hemodynamic forces and rupture status of IAs, the role of hemodynamic forces in unruptured IAs with AWE is poorly understood. The authors investigated the role and implications of abnormal hemodynamics related to aneurysm pathophysiology in patients with AWE in unruptured IAs.

METHODS

Twenty-five patients who had undergone digital subtraction angiography (DSA) and VWMRI studies from September 2016 to September 2017 were included, resulting in 22 patients with 25 IAs, 9 with and 16 without AWE. High-resolution CFD models of hemodynamics were created from DSA images. Univariate and multivariate analyses were performed to investigate the association between AWE and conventional morphological and hemodynamic parameters. Normalized MRI signal intensity was quantified and quantitatively associated with wall shear stresses (WSSs) for the entire aneurysm sac, and in regions of low, intermediate, and high WSS.

RESULTS

The AWE group had lower WSS (p < 0.01) and sac-averaged velocity (p < 0.01) and larger aneurysm size (p < 0.001) and size ratio (p = 0.0251) than the non-AWE group. From multivariate analysis of both hemodynamic and morphological factors, only low WSS was found to be independently associated with AWE. Sac-averaged normalized MRI signal intensity correlated with WSS and was significantly different in regions of low WSS compared to regions of intermediate (p = 0.018) and high (p < 0.001) WSS.

CONCLUSIONS

The presence of AWE was associated with morphological and hemodynamic factors related to rupture risk. Low WSS was found to be an independent predictor of AWE. Our findings support the hypothesis that low WSS in IAs with AWE may indicate a growth and remodeling process that may predispose such aneurysms to rupture; however, a causality between the two cannot be established.

Comparing Morphology and Hemodynamics of Stable-versus-Growing and Grown Intracranial Aneurysms

BACKGROUND AND PURPOSE

Aneurysm growth has been related to higher rupture risk. A better understanding of the characteristics related to growth may assist in the treatment decisions of unruptured intracranial aneurysms. This study aimed to identify morphologic and hemodynamic characteristics associated with aneurysm growth and to determine whether these characteristics deviate further from those of stable aneurysms after growth.

MATERIALS AND METHODS

We included 81 stable and 56 growing aneurysms. 3D vascular models were segmented on CTA, MRA, or 3D rotational angiographic images. With these models, we performed computational fluid dynamics simulations. Morphologic (size, size ratios, and shape) and hemodynamic (inflow, vorticity, shear stress, oscillatory shear index, flow instability) characteristics were automatically calculated. We compared the characteristics between aneurysms that were stable and those that had grown at baseline and final imaging. The significance level after Bonferroni correction was P < .002.

RESULTS

At baseline, no significant differences between aneurysms that were stable and those that had grown were detected (P > .002). Significant differences between aneurysms that were stable and those that had grown were seen at the final imaging for shear rate, aneurysm velocity, vorticity, and mean wall shear stress (P < .002). The latter was 11.5 (interquartile range, 5.4-18.8 dyne/cm²) compared with 17.5 (interquartile range, 11.2-29.9 dyne/cm²) in stable aneurysms (P = .001). Additionally, a trend toward lower area weighted average Gaussian curvature in aneurysms that had grown was observed with a median of 6.0 (interquartile range, 3.2-10.7 cm^-2) compared with 10.4 (interquartile range, 5.0-21.2 cm^-2) in stable aneurysms (P = .004).

CONCLUSIONS

Morphologic and hemodynamic characteristics at baseline were not associated with aneurysm growth in our population. After growth, almost all indices increase toward values associated with higher rupture risks. Therefore, we stress the importance of longitudinal imaging and repeat risk assessment in unruptured aneurysms.

Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture

BACKGROUND

Machine learning (ML) has been increasingly used in medicine and neurosurgery. We sought to determine whether ML models can distinguish ruptured from unruptured aneurysms and identify features associated with rupture.

METHODS

We performed a retrospective review of patients with intracranial aneurysms detected on vascular imaging at our institution between 2002 and 2018. The dataset was used to train 3 ML models (random forest, linear support vector machine [SVM], and radial basis function kernel SVM). Relative contributions of individual predictors were derived from the linear SVM model.

RESULTS

Complete data were available for 845 aneurysms in 615 patients. Ruptured aneurysms (n = 309, 37%) were larger (mean 6.51 mm vs. 5.73 mm; P = 0.02) and more likely to be in the posterior circulation (20% vs. 11%; P < 0.001) than unruptured aneurysms. Area under the receiver operating curve was 0.77 for the linear SVM, 0.78 for the radial basis function kernel SVM models, and 0.81 for the random forest model. Aneurysm location and size were the 2 features that contributed most significantly to the model. Posterior communicating artery, anterior communicating artery, and posterior inferior cerebellar artery locations were most highly associated with rupture, whereas paraclinoid and middle cerebral artery locations had the strongest association with unruptured status.

CONCLUSIONS

ML models are capable of accurately distinguishing ruptured from unruptured aneurysms and identifying features associated with rupture. Consistent with prior studies, location and size show the strongest association with aneurysm rupture.

Automated Cutting Plane Positioning for Intracranial Aneurysm Quantification

OBJECTIVE

Aneurysm rupture risk can be assessed by its morphologic and hemodynamics features extracted based on angiographic images. Feature extraction entails aneurysm isolation, typically by manually positioning a cutting plane (MCP). To eliminate intra- and inter-rater variabilities, we propose automatic cutting plane (ACP) positioning based on the analysis of vascular surface mesh.

METHODS

Innovative Hough-like and multi-hypothesis-based detection of aneurysm center, parent vessel inlets, and centerlines were proposed. These were used for initialization and iterative ACP positioning by geometry-inspired cost function optimization. For validation and baseline comparison, we tested MCP and manual neck curve-based isolation. Isolated aneurysm morphology was characterized by size, dome height, aspect ratio, and nonsphericity index.

RESULTS

Methods were applied to 55 intracranial saccular aneurysms from two sites, involving 3-D digital subtraction angiography, computed tomography angiography, and magnetic resonance angiography modalities. Isolation based on ACP resulted in smaller average inter-curve distances (AICDs), compared to those obtained by MCP. One case had AICD higher than 1.0 mm, while 90% of cases had AICD 0.5 mm. Intra- and inter-rater AICD variability of manual neck curves was higher compared to MCP, validating its robustness for clinical purposes.

CONCLUSION

The ACP method achieved high accuracy and reliability of aneurysm isolation, also confirmed by expert visual analysis. So extracted morphologic features were in good agreement with MCP-based ones, therefore, ACP has great potential for aneurysm morphology and hemodynamics quantification in clinical applications.

SIGNIFICANCE

The novel method is angiographic modality agnostic; it delivers repeatable isolation important in follow-up aneurysm assessment; its performance is comparable to MCP; and re-evaluation is fast and simple.

Identification of Hostile Hemodynamics and Geometries of Cerebral Aneurysms: A Case-Control Study

BACKGROUND AND PURPOSE

Hostile hemodynamic conditions and geometries are thought to predispose aneurysms for instability and rupture. This study compares stable, unstable, and ruptured aneurysms while controlling for location and patient characteristics.

MATERIALS AND METHODS

The hemodynamics and geometries of 165 stable, 65 unstable, and 554 ruptured aneurysms were compared. Hemodynamics was modeled using image-based computational fluid dynamics. Case-control pairs were selected matching aneurysm location, patient age, and sex. Paired Wilcoxon tests were used to compare hemodynamic and geometric variables among different aneurysm groups. The pairing was repeated 100 times, and the combined P values were calculated and adjusted for multiple testing.

RESULTS

Ruptured aneurysms had lower minimum wall shear stress (P = .03), higher maximum wall shear stress (P = .03), more concentrated (P = .03) and mean oscillatory shear stress (P = .03), higher maximum velocity (P = .03), and more complex flows (vortex core-line length, P = .03) than stable aneurysms. Similarly, unstable aneurysms had more concentrated shear stress (P = .04) and more complex flows (vortex core-line length, P = .04) than stable aneurysms. Compared with stable aneurysms, ruptured aneurysms were larger (size ratio, aneurysm size/vessel size, P = .03), more elongated (aspect ratio, P = .03), and irregular (nonsphericity index, P = .03). Similarly, unstable aneurysms were larger (size ratio, P = .04), more elongated (aspect ratio, P = .04), and irregular (bulge location, P = .04; area-weighted Gaussian curvature; P = .04) than stable aneurysms. No significant differences were found between unstable and ruptured aneurysms.

CONCLUSIONS

Unstable and ruptured aneurysms have more complex flows with concentrated wall shear stress and are larger, more elongated, and irregular than stable aneurysms, independent of aneurysm location and patient sex and age.