Cerebral Aneurysm Morphology Before and After Rupture

A simple and effective machine learning model for predicting the stability of intracranial aneurysms using CT angiography

Background

It is vital to accurately and promptly distinguish unstable from stable intracranial aneurysms (IAs) to facilitate treatment optimization and avoid unnecessary treatment. The aim of this study is to develop a simple and effective predictive model for the clinical evaluation of the stability of IAs.

Methods

In total, 1,053 patients with 1,239 IAs were randomly divided the dataset into training (70%) and internal validation (30%) datasets. One hundred and ninety seven patients with 229 IAs from another hospital were evaluated as an external validation dataset. The prediction models were developed using machine learning based on clinical information, manual parameters, and radiomic features. In addition, a simple model for predicting the stability of IAs was developed, and a nomogram was drawn for clinical use.

Results

Fourteen machine learning models exhibited excellent classification performance. Logistic regression Model E (clinical information, manual parameters, and radiomic shape features) had the highest AUC of 0.963 (95% CI 0.943-0.980). Compared to manual parameters, radiomic features did not significantly improve the identification of unstable IAs. In the external validation dataset, the simplified model demonstrated excellent performance (AUC = 0.950) using only five manual parameters.

Conclusion

Machine learning models have excellent potential in the classification of unstable IAs. The manual parameters from CTA images are sufficient for developing a simple and effective model for identifying unstable IAs.

Verification of a simplified aneurysm dimensionless flow parameter to predict intracranial aneurysm rupture status

OBJECTIVES

Aneurysm number (An) is a novel prediction tool utilizing parameters of pulsatility index (PI) and aneurysm geometry. An has been shown to have the potential to differentiate intracranial aneurysm (IA) rupture status. The objective of this study is to investigate the feasibility and accuracy of An for IA rupture status prediction using Australian based clinical data.

METHODS

A retrospective study was conducted across three tertiary referral hospitals between November 2017 and November 2020 and all saccular IAs with known rupture status were included. Two sets of An values were calculated based on two sets of PI values previously reported in the literature.

RESULTS

Five hundred and four IA cases were included in this study. The results demonstrated no significant difference between ruptured and unruptured status when using An ≥1 as the discriminator. Further analysis showed no strong correlation between An and IA subtypes. The area under the curve (AUC) indicated poor performance in predicting rupture status (AUC1 = 0.55 and AUC2 = 0.56).

CONCLUSIONS

This study does not support An ≥1 as a reliable parameter to predict the rupture status of IAs based on a retrospective cohort. Although the concept of An is supported by hemodynamic aneurysm theory, further research is needed before it can be applied in the clinical setting.

ADVANCES IN KNOWLEDGE

This study demonstrates that the novel prediction tool, An, proposed in 2020 is not reliable and that further research of this hemodynamic model is needed before it can be incorporated into the prediction of IA rupture status.

Optimizing timing for quantification of intracranial aneurysm enhancement: a multi-phase contrast-enhanced vessel wall MRI study

OBJECTIVES

Aneurysm wall enhancement (AWE) on high-resolution contrast-enhanced vessel wall MRI (VWMRI) is an emerging biomarker for intracranial aneurysms (IAs) stability. Quantification methods of AWE in the literature, however, are variable. We aimed to determine the optimal post-contrast timing to quantify AWE in both saccular and fusiform IAs.

MATERIALS AND METHODS

Consecutive patients with unruptured IAs were prospectively recruited. VWMRI was acquired on 1 pre-contrast and 4 consecutive post-contrast phases (each phase was 9 min). Signal intensity values of cerebrospinal fluid (CSF) and aneurysm wall on pre- and 4 post-contrast phases were measured to determine the aneurysm wall enhancement index (WEI). AWE was also qualitatively analyzed on post-contrast images using previous grading criteria. The dynamic changes of AWE grade and WEI were analyzed for both saccular and fusiform IAs.

RESULTS

Thirty-four patients with 42 IAs (27 saccular IAs and 15 fusiform IAs) were included. The changes in AWE grade occurred in 8 (30%) saccular IAs and 6 (40%) in fusiform IAs during the 4 post-contrast phases. The WEI of fusiform IAs decreased 22.0% over time after contrast enhancement (p = 0.009), while the WEI of saccular IAs kept constant during the 4 post-contrast phases (p > 0.05).

CONCLUSIONS

When performing quantitative analysis of AWE, acquiring post-contrast VWMRI immediately after contrast injection achieves the strongest AWE for fusiform IAs. While the AWE degree is stable for 36 min after contrast injection for saccular IAs.

CLINICAL RELEVANCE STATEMENT

The standardization of imaging protocols and analysis methods for AWE will be helpful for imaging surveillance and further treatment decisions of patients with unruptured IAs.

KEY POINTS

Imaging protocols and measurements of intracranial aneurysm wall enhancement are reported heterogeneously. Aneurysm wall enhancement for fusiform intracranial aneurysms (IAs) is strongest immediately post-contrast, and stable for 36 min for saccular IAs. Future multi-center studies should investigate aneurysm wall enhancement as an emerging marker of aneurysm growth and rupture.

Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms

Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.

Associations between morphological parameters and ruptured anterior communicating artery aneurysm: A propensity score-matched, single center, case-control study

BACKGROUND

To identify an association between morphological parameters and the rupture risk of anterior communicating artery (ACoA) aneurysms using propensity score matching (PSM).

METHODS

Data for 109 patients with ACoA aneurysms treated from January 2018 to October 2021 were reviewed; 94 patients were enrolled. The geometrical parameters of the ACoA aneurysms were measured and calculated using three-dimensional reconstructed digital subtraction angiography images. The aneurysms' morphological parameters were analyzed using a propensity score for six factors (age, sex, excess alcohol intake, smoking, hypertension, diabetes mellitus). Univariate logistic regression was used to analyze the relationship between the aneurysms' morphological parameters and rupture risk.

RESULTS

Twenty-five patients each with or without ruptured aneurysms were selected. After matching, no statistically significant differences were seen between the groups in their baseline characteristics. Aneurysm neck size (p = 0.038) was higher in the unruptured group than that in the ruptured group, and the dome-to-neck ratio (D/N; p = 0.009) and aspect ratio (AR; p = 0.003) were higher in the ruptured group than those in the unruptured group. Univariable logistic regression analysis demonstrated that ACoA aneurysm rupture was associated with AR (odds ratio: 8.047; 95% confidence interval: 1.569-41.213; p = 0.012) and D/N (odds ratio: 4.253; 95% confidence interval: 1.228-14.731; p = 0.022). The areas under the receiver operating characteristic curves for AR and D/N were 0.746 and 0.715, respectively.

CONCLUSIONS

After PSM, ACoA aneurysms with higher AR and D/N, and smaller neck size were more likely to rupture. AR may be a much more important predictor of aneurysm rupture than other predictors.

Three-Dimensional Morphological Change of Intracranial Aneurysms Before and Around Rupture

BACKGROUND AND OBJECTIVES

Patients with an unruptured intracranial aneurysm often undergo periodic imaging to detect potential aneurysm growth, which is associated with an increased rupture risk. Because prediction of rupture based on growth is moderate, morphological changes have gained interest as a risk factor for rupture. We studied 3-dimensional-quantified morphological changes over time during radiological monitoring before rupture and around rupture.

METHODS

In this retrospective observational study, we identified aneurysms that ruptured during follow-up, with imaging available for at least 2 time points before rupture and one after rupture. For each time point, we obtained 8 morphological parameters: 2-dimensional size, volume, surface area, compactness 1 and 2, sphericity, elongation, and flatness. Morphological changes before rupture and around rupture were log-transformed, scaled, and analyzed with linear mixed-effects models.

RESULTS

We included 16 aneurysms in 16 patients who were imaged between 2004 and 2021. In the time period before rupture (median follow-up duration 1200 days, IQR 736-1340), 3 size-related morphological parameters increased: 2-dimensional size (estimated mean change 0.44, 95% CI 0.24-0.65), volume (estimated mean change 0.34, 95% CI 0.12-0.56), and surface area (0.33, 95% CI 0.11-0.54). In the period around rupture (median follow-up duration 407 days, IQR 148-719), these parameters further increased. In addition, 5 morphological parameters (compactness 1 and 2, sphericity, elongation, and flatness) decreased around rupture but not before rupture.

CONCLUSION

Change in aneurysm volume and surface area may be novel risk factors for rupture. Because most morphological parameters changed around but not before rupture, morphological changes during these 2 periods should be regarded as different processes. This implies that postrupture morphology should not be used as a surrogate for prerupture morphology in rupture prediction models.

Prevalence and prognosis of acute ischemic stroke coexisting with unruptured intracranial aneurysms

Objectives

The prevalence of unruptured intracranial aneurysms (UIAs) in the acute ischemic stroke (AIS) cohort is probably higher than in the general population. This study investigated the prevalence of UIAs in AIS patients and the management risk and prognosis when treating AIS.

Methods

From January 2020 to January 2023, we conducted a single-center retrospective study at Tianjin Huanhu Hospital. Each patient underwent both brain MRI and MRA/CTA to diagnose AIS and UIAs. Clinical, radiologic, and therapeutic data during hospitalization and prognosis were analyzed. Propensity-score matching (PSM) was performed to evaluate the risk of in-hospital adverse events, unfavorable outcomes at discharge when receiving post-stroke treatment and stroke recurrence.

Results

In all, 2,181 AIS patients were included, of whom 270 had UIAs (12.4%; 95%CI 11.0-13.8%). From the unmatched and matched cohort, the incidence of in-hospital adverse events and unfavorable outcomes at discharge in patients with UIAs were not significantly different; the risk of stroke recurrence was significantly higher in patients with UIAs than in those without (unmatched: aHR, 1.71 [1.08-2.70]; matched: aHR, 2.55 [1.16-5.58]). Multivariable Cox regression models showed that aneurysm size and the presence of homoregional infarction associated with higher risk of recurrence (unmatched: aHR, 1.31 [1.21-1.41] and aHR, 3.50 [1.52-8.10]; matched: aHR, 1.28 [1.18-1.40]; p < 0.001 and aHR, 3.71 [1.12-12.34]).

Conclusion

The UIAs may not increase the risk of in-hospital adverse events and unfavorable outcomes at discharge in receiving post-stroke treatment, but it may associated with a high risk of stroke recurrence.

Toward a novel soft robotic system for minimally invasive interventions

PURPOSE

During minimally invasive surgery, surgeons maneuver tools through complex anatomies, which is difficult without the ability to control the position of the tools inside the body. A potential solution for a substantial portion of these procedures is the efficient design and control of a pneumatically actuated soft robot system.

METHODS

We designed and evaluated a system to control a steerable catheter tip. A macroscale 3D printed catheter tip was designed to have two separately pressurized channels to induce bending in two directions. A motorized hand controller was developed to allow users to control the bending angle while manually inserting the steerable tip. Preliminary characterization of two catheter tip prototypes was performed and used to map desired angle inputs into pressure commands.

RESULTS

The integrated robotic system allowed both a novice and a skilled surgeon to position the steerable catheter tip at the location of cylindrical targets with sub-millimeter accuracy. The novice was able to reach each target within ten seconds and the skilled surgeon within five seconds on average.

CONCLUSION

This soft robotic system enables its user to simultaneously insert and bend the pneumatically actuated catheter tip with high accuracy and in a short amount of time. These results show promise concerning the development of a soft robotic system that can improve outcomes in minimally invasive interventions.

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.

Hemodynamics of anterior circulation intracranial aneurysms with daughter blebs: investigating the multidirectionality of blood flow fields

Recent advances in diagnostic neuroradiological imaging, allowed the detection of unruptured intracranial aneurysms (IAs). The shape - irregular or multilobular - of the aneurysmal dome, is considered as a possible rupture risk factor, independently of the size, the location and patient medical background. Disturbed blood flow fields in particular is thought to play a key role in IAs progression. However, there is an absence of widely-used hemodynamic indices to quantify the extent of a multi-directional disturbed flow. We simulated blood flow in twelve patient-specific anterior circulation unruptured intracranial aneurysms with daughter blebs utilizing the spectral/hp element framework Nektar++. We simulated three cardiac cycles using a volumetric flow rate waveform while we considered blood as a Newtonian fluid. To investigate the multidirectionality of the blood flow fields, besides the time-averaged wall shear stress (TAWSS), we calculated the oscillatory shear index (OSI), the relative residence time (RRT) and the time-averaged cross flow index (TACFI). Our CFD simulations suggest that in the majority of our vascular models there is a formation of complex intrasaccular flow patterns, resulting to low and highly oscillating WSS, especially in the area of the daughter blebs. The existence of disturbed multi-directional blood flow fields is also evident by the distributions of the RRT and the TACFI. These findings further support the theory that IAs with daughter blebs are linked to a potentially increased rupture risk.

Morphological evaluation of the risk of posterior communicating artery aneurysm rupture: a mirror aneurysm model

OBJECTIVE

The aim of this study was to use morphological parameters of mirror posterior communicating artery (PCoA) aneurysms to evaluate aneurysm rupture risk.

METHODS

The morphological parameters of 45 pairs of ruptured mirror PCoA aneurysms were analyzed. Conditional univariate and multivariate logistic regression of the following paired morphological parameters was performed: aneurysm with a daughter sac, aneurysm height, aneurysm width, neck width, internal carotid artery diameter, PCoA diameter, flow angle, PCoA angle, aspect ratio, bottleneck factor, size ratio, height/width ratio, fetal posterior cerebral artery, and aneurysm with height > width. A scoring system was established according to the odds ratios (ORs). The receiver operating characteristic was used to test the prediction accuracy of this scoring system in the authors' database of 523 PCoA aneurysms and the threshold value was used to define higher risk.

RESULTS

Aneurysm width (OR 1.676, p = 0.014), aneurysm with daughter sac (OR 7.775, p = 0.016), and aneurysm with height > width (OR 9.067, p = 0.012) were independent risk factors for rupture. The scoring system consisted of aneurysm width (1 point per mm), aneurysm with a daughter sac (5 points), and aneurysm with height > width (5 points). The area under the curve (AUC) of the scoring system was 0.842, and its threshold value was 7.97. A score ≥ 8 points was defined as higher risk. The AUC using this definition was 0.802.

CONCLUSIONS

Aneurysm width, aneurysms with height > width, and aneurysms with a daughter sac were independent risk factors for PCoA aneurysm rupture. The scoring system devised in this study accurately predicts rupture risk.

Predicting the growth of middle cerebral artery bifurcation aneurysms using differences in the bifurcation angle and inflow coefficient

OBJECTIVE

Growing intracranial aneurysms (IAs) are prone to rupture. Previous cross-sectional studies using postrupture morphology have shown the morphological or hemodynamic features related to IA rupture. Yet, which morphological or hemodynamic differences of the prerupture status can predict the growth and rupture of smaller IAs remains unknown. The purpose of this longitudinal study was to investigate the effects of morphological features and the hemodynamic environment on the growth of IAs at middle cerebral artery (MCA) bifurcations during the follow-up period.

METHODS

One hundred two patients with MCA M1–2 bifurcation saccular IAs who underwent follow-up for more than 2 years at the authors’ institutions between 2011 and 2019 were retrospectively identified. During the follow-up period, cases involving growth of MCA IAs were assigned to the event group, and those with MCA IAs unchanged in size were assigned to the control group. The morphological parameters examined were aneurysmal neck length, dome height, aspect ratio and volume, M1 and M2 diameters and their ratio, and angle configurations among M1, M2, and the aneurysm. Hemodynamic parameters were flow rate and wall shear stress in M1, M2, and the aneurysm, including the aneurysmal inflow rate coefficient (AIRC), defined as the ratio of the aneurysmal inflow rate to the M1 flow rate. Those parameters were compared statistically between the two groups. Correlations between morphological and hemodynamic parameters were also examined.

RESULTS

Eighty-three of 102 patients were included: 25 with growing MCA IAs (event group) and 58 with stable MCA IAs (control group). The median patient age at initial diagnosis was 66.9 (IQR 59.8–72.3) years. The median follow-up period was 48.5 (IQR 36.5–65.6) months. Both patient age and the AIRC were significant independent predictors of the growth of MCA IAs. Moreover, the AIRC was strongly correlated with sharper bifurcation and inflow angles, as well as wider inclination angles between the M1 and M2 arteries.

CONCLUSIONS

The AIRC was a significant independent predictor of the growth of MCA IAs. Sharper bifurcation and inflow angles and wider inclination angles between the M1 and M2 arteries were correlated with the AIRC. MCA IAs with such a bifurcation configuration are more prone to grow and rupture.

Reliability of using generic flow conditions to quantify aneurysmal haemodynamics: A comparison against simulations incorporating boundary conditions measured in vivo

BACKGROUND AND OBJECTIVES

Initiation, growth, and rupture of intracranial aneurysms are believed to be closely related to their local haemodynamic environment. While haemodynamics can be characterised by use of computational fluid dynamics (CFD), its reliability depends heavily upon accurate assumption of the boundary conditions. Herein, we compared the simulated aneurysmal haemodynamics obtained by use of generic boundary conditions against those obtained under flow conditions measured in vivo.

METHODS

We prospectively recruited 19 patients with intracranial aneurysms requiring 3-dimensional rotational angiography, during which blood pressure at the internal carotid artery was probed by catheter and flowrate measured by a dedicated software tool. Using these flow conditions measured in vivo, we quantified the aneurysmal haemodynamics for each patient by CFD, and then compared the results with those derived from a generic condition reported in the literature, in terms of the time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and percentage of the intra-aneurysmal flow (PIAF). In addition, the effects on aneurysmal haemodynamics of different outflow strategies (splitting method vs. Murray's Law) and simulation schemes (transient vs. steady-state) relative to each flow condition were also assessed.

RESULTS

Differences in the simulated TAWSS (-6.08 ± 10.64 Pa, p = 0.001), OSI (0.06 ± 0.13, p = 0.001), and PIAF (-0.05 ± 0.20, p = 0.012) between the patient-specific and generic boundary conditions were found to be statistically significant, in contrast to that in the RRT (49 ± 307 Pa^-1, p = 0.062). Outflow strategies did not yield statistically significant differences in any of the investigated parameters (all p > 0.05); rather, the resulting parameters were found to be in good correlations (all r > 0.71, p < 0.001). Difference between the aneurysmal TAWSS and the WSS derived from cycle-averaged flowrate condition was found to be minor (0.66 ± 1.36 Pa, p = 0.000), so was that between PIAFs obtained respectively from the transient and steady-state simulations (0.02 ± 0.05, p = 0.000).

CONCLUSIONS

Incorporating into simulation the patient-specific boundary conditions is critical for CFD to characterise aneurysmal haemodynamics, while outflow strategies may not introduce significant uncertainties. Steady-state simulation incorporating the cycle-averaged flow condition may produce unbiased WSS and PIAF compared to the transient analysis.

Artificial intelligence aneurysm measurement tool finds growth in all aneurysms that ruptured during conservative management

BACKGROUND

Cerebral aneurysm rupture is associated with high rates of morbidity and mortality. Detecting aneurysms at high risk of rupture is critical in management decision making. Rupture risk has traditionally been associated with size-measured as a maximum dimension. However, aneurysms are morphologically dynamic, a characteristic ignored by large prospective aneurysm risk studies. Manual measurement is challenging and fraught with error. We used an artificial intelligence (AI) measurement tool to study aneurysms that ruptured during conservative management to detect changes in size not appreciated by manual linear measurement.

METHODS

A single practice database with >5000 aneurysms was queried. Patients followed conservatively for an unruptured aneurysm were identified using appropriate diagnosis codes. This cohort was screened for subsequent rupture using procedure codes. Only patients with two vascular imaging studies before rupture were included.

RESULTS

Five patients met the criteria. All patients had aneurysm enlargement, two of which were not detected from manual linear measurements, including adjudication and analysis, during a multidisciplinary neurovascular conference in a high volume practice. Maximum dimension increased at a minimum of 1.8% (range 1.8-63.3%) from the first scan to the last, and aneurysm volume increased at a minimum of 5.9% (5.9-385.5%), highlighting the importance of volumetric measurement.

CONCLUSIONS

AI-enabled volumetric measurements are more sensitive to changes in size and detected enlargement in all aneurysms that ruptured during conservative management. This finding has major implications for clinical practice and methods used for interval aneurysm measurement in patients being conservatively followed.

Stability of unruptured intracranial aneurysms in the anterior circulation: nomogram models for risk assessment

OBJECTIVE

The probable stability of the lesion is critical in guiding treatment decisions in unruptured intracranial aneurysms (IAs). The authors aimed to develop multidimensional predictive models for the stability of unruptured IAs.

METHODS

Patients with unruptured IAs in the anterior circulation were prospectively enrolled and regularly followed up. Clinical data were collected, IA morphological features were assessed, and adjacent hemodynamic features were quantified with patient-specific computational fluid dynamics modeling. Based on multivariate logistic regression analyses, nomograms incorporating these factors were developed in a primary cohort (patients enrolled between January 2017 and February 2018) to predict aneurysm rupture or growth within 2 years. The predictive accuracies of the nomograms were compared with the population, hypertension, age, size, earlier rupture, and site (PHASES) and earlier subarachnoid hemorrhage, location, age, population, size, and shape (ELAPSS) scores and validated in the validation cohort (patients enrolled between March and October 2018).

RESULTS

Among 231 patients with 272 unruptured IAs in the primary cohort, hypertension, aneurysm location, irregular shape, size ratio, normalized wall shear stress average, and relative resident time were independently related to the 2-year stability of unruptured IAs. The nomogram including clinical, morphological, and hemodynamic features (C+M+H nomogram) had the highest predictive accuracy (c-statistic 0.94), followed by the nomogram including clinical and morphological features (C+M nomogram; c-statistic 0.89), PHASES score (c-statistic 0.68), and ELAPSS score (c-statistic 0.58). Similarly, the C+M+H nomogram had the highest predictive accuracy (c-statistic 0.94) in the validation cohort (85 patients with 97 unruptured IAs).

CONCLUSIONS

Hemodynamics have predictive values for 2-year stability of unruptured IAs treated conservatively. Multidimensional nomograms have significantly higher predictive accuracies than conventional risk prediction scores.

Small ruptured intracranial aneurysms are overrepresented at the anterior and posterior communicating artery: Results of a multiple regression analysis

Background:

Anterior communicating artery (AcomA) represents the most common location for ruptured intracranial aneurysms (rIAs). Approximately 50% of all rIAs are smaller than 7 mm, but factors that lead to rupture are multifactorial. The study investigates whether AcomA location represents an independent risk factor for small size at time of rupture (<7 mm) in a cohort of aneurysmal subarachnoid hemorrhage (aSAH) when controlling for known risk factors.

Methods:

The aSAH cohort was retrospectively searched from our institution charts. The cohort was dichotomized into small aneurysms (<7 mm) or large aneurysms (≥7 mm). Risk factors for rupture were identified according to the unruptured intracranial aneurysm treatment score (UIATS). These were sex, age, location, smoking, hypertension, alcohol abuse, aneurysm morphology, multiplicity, previous SAH, and family history. With size as independent variable, a multiple regression analysis was performed including UIATS risk factors.

Results:

One-hundred and seventy-six patients were included in the study. About 49.4% of the aneurysms were <7 mm. Multiple regression analysis demonstrated that aneurysms located at AcomA and posterior communicating artery (PcomA) was significantly more frequent smaller than 7 mm, compared to middle cerebral artery (P = 0.006), internal carotid artery (other than PcomA) (P = 0.013), and posterior circulation (P = 0.017), when controlling for risk factors.

Conclusion:

Ruptured AcomA and PcomA aneurysms are more frequent smaller than 7 mm compared to other locations. Patients with unruptured UIA at either AcomA or PcomA may be at increased risk of rupture even if the size of the aneurysm is small. Further studies are needed to confirm this finding.

Shape Trumps Size: Image-Based Morphological Analysis Reveals That the 3D Shape Discriminates Intracranial Aneurysm Disease Status Better Than Aneurysm Size

Background

To date, it remains difficult for clinicians to reliably assess the disease status of intracranial aneurysms. As an aneurysm's 3D shape is strongly dependent on the underlying formation processes, it is believed that the presence of certain shape features mirrors the disease status of the aneurysm wall. Currently, clinicians associate irregular shape with wall instability. However, no consensus exists about which shape features reliably predict instability. In this study, we present a benchmark to identify shape features providing the highest predictive power for aneurysm rupture status.

Methods

3D models of aneurysms were extracted from medical imaging data (3D rotational angiographies) using a standardized protocol. For these aneurysm models, we calculated a set of metrics characterizing the 3D shape: Geometry indices (such as undulation, ellipticity and non-sphericity); writhe- and curvature-based metrics; as well as indices based on Zernike moments. Using statistical learning methods, we investigated the association between shape features and aneurysm disease status. This processing was applied to a clinical dataset of 750 aneurysms (261 ruptured, 474 unruptured) registered in the AneuX morphology database. We report here statistical performance metrics [including the area under curve (AUC)] for morphometric models to discriminate between ruptured and unruptured aneurysms.

Results

The non-sphericity index NSI (AUC = 0.80), normalized Zernike energies ZNsurf (AUC = 0.80) and the modified writhe-index W¯meanL1 (AUC = 0.78) exhibited the strongest association with rupture status. The combination of predictors further improved the predictive performance (without location: AUC = 0.82, with location AUC = 0.87). The anatomical location was a good predictor for rupture status on its own (AUC = 0.78). Different protocols to isolate the aneurysm dome did not affect the prediction performance. We identified problems regarding generalizability if trained models are applied to datasets with different selection biases.

Conclusions

Morphology provided a clear indication of the aneurysm disease status, with parameters measuring shape (especially irregularity) being better predictors than size. Quantitative measurement of shape, alone or in conjunction with information about aneurysm location, has the potential to improve the clinical assessment of intracranial aneurysms.

Construction and Evaluation of Multiple Radiomics Models for Identifying the Instability of Intracranial Aneurysms Based on CTA

Background and Aims

Identifying unruptured intracranial aneurysm instability is crucial for therapeutic decision-making. This study aims to evaluate the role of Radiomics and traditional morphological features in identifying aneurysm instability by constructing and comparing multiple models.

Materials and Methods

A total of 227 patients with 254 intracranial aneurysms evaluated by CTA were included. Aneurysms were divided into unstable and stable groups using comprehensive criteria: the unstable group was defined as aneurysms with near-term rupture, growth during follow-up, or caused compressive symptoms; those without the aforementioned conditions were grouped as stable aneurysms. Aneurysms were randomly divided into training and test sets at a 1:1 ratio. Radiomics and traditional morphological features (maximum diameter, irregular shape, aspect ratio, size ratio, location, etc.) were extracted. Three basic models and two integrated models were constructed after corresponding statistical analysis. Model A used traditional morphological parameters. Model B used Radiomics features. Model C used the Radiomics features related to aneurysm morphology. Furthermore, integrated models of traditional and Radiomics features were built (model A+B, model A+C). The area under curves (AUC) of each model was calculated and compared.

Results

There were 31 (13.7%) patients harboring 36 (14.2%) unstable aneurysms, 15 of which ruptured post-imaging, 16 with growth on serial imaging, and 5 with compressive symptoms, respectively. Four traditional morphological features, six Radiomics features, and three Radiomics-derived morphological features were identified. The classification of aneurysm stability was as follows: the AUC of the training set and test set in models A, B, and C are 0.888 (95% CI 0.808–0.967) and 0.818 (95% CI 0.705–0.932), 0.865 (95% CI 0.777–0.952) and 0.739 (95% CI 0.636–0.841), 0.605(95% CI 0.470–0.740) and 0.552 (95% CI 0.401–0.703), respectively. The AUC of integrated Model A+B was numerically slightly higher than any single model, whereas Model A+C was not.

Conclusions

A radiomics and traditional morphology integrated model seems to be an effective tool for identifying intracranial aneurysm instability, whereas the use of Radiomics-derived morphological features alone is not recommended. Radiomics-based models were not superior to the traditional morphological features model.

Validation of the predictive accuracy of “clinical + morphology nomogram” for the rebleeding risk of ruptured intracranial aneurysms after admission

Background

Rebleeding can cause a catastrophic outcome after aneurysmal subarachnoid hemorrhage. A clinical + morphology nomogram was promoted in our previous study to assist in discriminating the rupture intracranial aneurysms (RIAs) with a high risk of rebleeding. The aim of this study was to validate the predictive accuracy of this nomogram model.

Method

The patients with RIAs in two medical centers from December 2020 to September 2021 were retrospectively reviewed, whose clinical and morphological parameters were collected. The Cox regression model was employed to identify the risk factors related to rebleeding after their admission. The predicting accuracy of clinical + morphological nomogram, ELAPSS score and PHASES score was compared based on the area under the curves (AUCs).

Results

One hundred thirty-eight patients with RIAs were finally included in this study, 20 of whom suffering from rebleeding after admission. Hypertension (hazard ratio (HR), 2.54; a confidence interval of 95% (CI), 1.01–6.40; P = 0.047), bifurcation (HR, 3.88; 95% CI, 1.29–11.66; P = 0.016), and AR (HR, 2.68; 95% CI, 1.63–4.41; P < 0.001) were demonstrated through Cox regression analysis as the independent risk factors for rebleeding after admission. The clinical + morphological nomogram had the highest predicting accuracy (AUC, 0.939, P < 0.01), followed by the bifurcation (AUC, 0.735, P = 0.001), AR (AUC, 0.666, P = 0.018), and ELAPSS score (AUC, 0.682, P = 0.009). Hypertension (AUC, 0.693, P = 0.080) or PHASES score (AUC, 0.577, P = 0.244) could not be used to predict the risk of rebleeding after admission. The calibration curve for the probability of rebleeding showed a good agreement between the prediction through clinical + morphological nomogram and actual observation.

Conclusion

Hypertension, bifurcation site, and AR were independent risk factors related to the rebleeding of RIAs after admission. The clinical + morphological nomogram could help doctors to identify the high-risk RIAs with a high predictive accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41016-022-00274-4.

Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction

OBJECTIVES

We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data.

METHOD

Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons.

RESULT

Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system.

CONCLUSION

Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture.

KEY POINTS

• A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.

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.

A stroke detection and discrimination framework using broadband microwave scattering on stochastic models with deep learning

Stroke poses an immense public health burden and remains among the primary causes of death and disability worldwide. Emergent therapy is often precluded by late or indeterminate times of onset before initial clinical presentation. Rapid, mobile, safe and low-cost stroke detection technology remains a deeply unmet clinical need. Past studies have explored the use of microwave and other small form-factor strategies for rapid stroke detection; however, widespread clinical adoption remains unrealized. Here, we investigated the use of microwave scattering perturbations from ultra wide-band antenna arrays to learn dielectric signatures of disease. Two deep neural networks (DNNs) were used for: (1) stroke detection (“classification network”), and (2) characterization of the hemorrhage location and size (“discrimination network”). Dielectric signatures were learned on a simulated cohort of 666 hemorrhagic stroke and control subjects using 2D stochastic head models. The classification network yielded a stratified K-fold stroke detection accuracy > 94% with an AUC of 0.996, while the discrimination network resulted in a mean squared error of < 0.004 cm and < 0.02 cm, for the stroke localization and size estimation, respectively. We report a novel approach to intelligent diagnostics using microwave wide-band scattering information thus circumventing conventional image-formation.

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. The segmentation quality was measured using the Jaccard index and a combination of different surface distance measures. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADA challenge. The U-Net-based detection solutions presented by the community show similar accuracy compared to experts (F2 score 0.92), with a small number of missed aneurysms with diameters smaller than 3.5 mm. In addition, the delineation of these structures, based on U-Net variations, is excellent, with a Jaccard score of 0.92. The rupture risk estimation methods achieved an F2 score of 0.71. The performance of the detection and segmentation solutions is equivalent to that of human experts. The best results are obtained in rupture risk estimation by combining different image-based, morphological, and computational fluid dynamic parameters using machine learning methods. Furthermore, we evaluated the best methods pipeline, from detecting and delineating the vessel dilations to estimating the risk of rupture. The chain of these methods achieves an F2-score of 0.70, which is comparable to applying the risk prediction to the ground-truth delineation (0.71).

Moments of Intra-Dome Velocity Distribution as Robust Predictors of Rupture Status in Cerebral Aneurysms

BACKGROUND

Wall shear stress (WSS), the spatial gradient of flow velocity at luminal surface, has been employed for aneurysmal hemodynamic analysis, but it is sensitive to surface irregularities and noise. We devised a volumetric approach to evaluate discriminant power of intra-dome flow velocity distribution and modal analysis in rupture status determination compared with previously described WSS analysis.

METHODS

Catheter three-dimensional rotational angiographic datasets matched for volume were segmented in 20 sidewall aneurysms (10 ruptured), computational fluid dynamics simulations were performed, and velocity distributions were extracted from mesh-independent isometric sampling followed by moment analysis (mean, variance, skewness, and kurtosis). Univariate and multivariate analysis was used to evaluate discriminant performance of velocity moments. Sensitivity of velocity moments and WSS was evaluated to bleb presence and surface irregularity using digital bleb removal and surface noise addition.

RESULTS

Velocity moments of ruptured aneurysms showed higher skewness (2.45 ± 0.57 vs. 1.36 ± 0.82, P = 0.003) and kurtosis (11.83 ± 4.77 vs. 6.05 ± 4.65, P = 0.01) with lower mean (0.019 ± 0.01 vs. 0.038 ± 0.02, P = 0.03) compared with unruptured lesions; in multivariate modeling, skewness alone emerged as best predictor (area under the curve = 0.88). Bleb removal increased low WSS by 548%, and surface noise decreased it by 85.8% while having a smaller (<7%) effect on velocity skewness and kurtosis.

CONCLUSIONS

High aneurysm dome flow velocity skewness and kurtosis suggest an exponential distribution in ruptured lesions, with high peaks at low velocities, consistent with areas of slow flow. In contrast to WSS-based techniques, this approach is robust against surface variations, with promising improved rupture status discriminant performance that requires further validation in expanded future studies.

On the prevalence of flow instabilities from high-fidelity computational fluid dynamics of intracranial bifurcation aneurysms

High-fidelity computational fluid dynamics (HF-CFD) has revealed the potential for high-frequency flow instabilities (aka "turbulent-like" flow) in intracranial aneurysms, consistent with classic in vivo and in vitro reports of bruits and/or wall vibrations. However, HF-CFD has typically been performed on limited numbers of cases, often with unphysiological inflow conditions or focused on sidewall-type aneurysms where flow instabilities may be inherently less prevalent. Here we report HF-CFD of 50 bifurcation aneurysm cases from the open-source Aneurisk model repository. These were meshed using quadratic finite elements having an average effective spatial resolution of 0.065 mm, and solved under physiologically-pulsatile flow conditions using a well-validated, minimally-dissipative solver with 20,000 time-steps per cardiac cycle Flow instability was quantified using the recently introduced spectral power index (SPI), which quantifies, from 0 to 1, the power associated with velocity fluctuations above those of the driving inflow waveform. Of the 50 cases, nearly half showed regions within the sac having SPI up to 0.5, often with non-negligible power into the 100's of Hz, and roughly 1/3 had sac-averaged SPI > 0.1. High SPI did not significantly predict rupture status in this cohort. Proper orthogonal decomposition of cases with highest SPI_avg revealed time-varying energetics consistent with transient turbulence. Our reported prevalence of high-frequency flow instabilities in HF-CFD modelling of aneurysms suggests that care must be taken to avoid routinely overlooking them if we are to understand the highly dynamic mechanical forces to which some aneurysm walls may be exposed, and their prevalence in vivo.

Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function <13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

Rebleeding of Ruptured Intracranial Aneurysm After Admission: A Multidimensional Nomogram Model to Risk Assessment

Objective

Rebleeding is recognized as the main cause of mortality after intracranial aneurysm rupture. Though timely intervention can prevent poor prognosis, there is no agreement on the surgical priority and choosing medical treatment for a short period after rupture. The aim of this study was to investigate the risk factors related to the rebleeding after admission and establish predicting models for better clinical decision-making.

Methods

The patients with ruptured intracranial aneurysms (RIAs) between January 2018 and September 2020 were reviewed. All patients fell to the primary and the validation cohort by January 2020. The hemodynamic parameters were determined through the computational fluid dynamics simulation. Cox regression analysis was conducted to identify the risk factors of rebleeding. Based on the independent risk factors, nomogram models were built, and their predicting accuracy was assessed by using the area under the curves (AUCs).

Result

A total of 577 patients with RIAs were enrolled in this present study, 86 patients of them were identified as undergoing rebleeding after admission. Thirteen parameters were identified as significantly different between stable and rebleeding aneurysms in the primary cohort. Cox regression analysis demonstrated that six parameters, including hypertension [hazard ratio (HR), 2.54; P = 0.044], bifurcation site (HR, 1.95; P = 0.013), irregular shape (HR, 4.22; P = 0.002), aspect ratio (HR, 12.91; P < 0.001), normalized wall shear stress average (HR, 0.16; P = 0.002), and oscillatory stress index (HR, 1.14; P < 0.001) were independent risk factors related to the rebleeding after admission. Two nomograms were established, the nomogram including clinical, morphological, and hemodynamic features (CMH nomogram) had the highest predicting accuracy (AUC, 0.92), followed by the nomogram including clinical and morphological features (CM nomogram; AUC, 0.83), ELAPSS score (AUC, 0.61), and PHASES score (AUC, 0.54). The calibration curve for the probability of rebleeding showed good agreement between prediction by nomograms and actual observation. In the validation cohort, the discrimination of the CMH nomogram was superior to the other models (AUC, 0.93 vs. 0.86, 0.71 and 0.48).

Conclusion

We presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the RIAs with high risk of rebleeding.

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.

Quantitative analysis of flow vortices: differentiation of unruptured and ruptured medium-sized middle cerebral artery aneurysms

BACKGROUND

Surgical intervention for unruptured intracranial aneurysms (IAs) carries inherent health risks. The analysis of "patient-specific" IA geometric and computational fluid dynamics (CFD) simulated wall shear stress (WSS) data has been investigated to differentiate IAs at high and low risk of rupture to help clinical decision making. Yet, outcomes vary among studies, suggesting that novel analysis could improve rupture characterization. The authors describe a CFD analytic method to assess spatiotemporal characteristics of swirling flow vortices within IAs to improve characterization.

METHODS

CFD simulations were performed for 47 subjects harboring one medium-sized (4-10 mm) middle cerebral artery (MCA) aneurysm with available 3D digital subtraction angiography data. Alongside conventional indices, quantified IA flow vortex spatiotemporal characteristics were applied during statistical characterization. Statistical supervised machine learning using a support vector machine (SVM) method was run with cross-validation (100 iterations) to assess flow vortex-based metrics' strength toward rupture characterization.

RESULTS

Relying solely on vortex indices for statistical characterization underperformed compared with established geometric characteristics (total accuracy of 0.77 vs 0.80) yet showed improvements over wall shear stress models (0.74). However, the application of vortex spatiotemporal characteristics into the combined geometric and wall shear stress parameters augmented model strength for assessing the rupture status of middle cerebral artery aneurysms (0.85).

CONCLUSIONS

This preliminary study suggests that the spatiotemporal characteristics of flow vortices within MCA aneurysms are of value to improve the differentiation of ruptured aneurysms from unruptured ones.

Analysis of Morphological-Hemodynamic Risk Factors for Aneurysm Rupture Including a Newly Introduced Total Volume Ratio

The purpose of this study was to evaluate morphological and hemodynamic factors, including the newly developed total volume ratio (TVR), in evaluating rupture risk of cerebral aneurysms using ≥7 mm sized aneurysms. Twenty-three aneurysms (11 unruptured and 12 ruptured) ≥ 7 mm were analyzed from 3-dimensional rotational cerebral angiography and computational fluid dynamics (CFD). Ten morphological and eleven hemodynamic factors of the aneurysms were qualitatively and quantitatively compared. Correlation analysis between morphological and hemodynamic factors was performed, and the relationship among the hemodynamic factors was analyzed. Morphological factors (ostium diameter, ostium area, aspect ratio, and bottleneck ratio) and hemodynamic factors (TVR, minimal wall shear stress of aneurysms, time-averaged wall shear stress of aneurysms, oscillatory shear index, relative residence time, low wall shear stress area, and ratio of low wall stress area) were statistically different between ruptured and unruptured aneurysms (p < 0.05). By simple regression analysis, the morphological factor aspect ratio and the hemodynamic factor TVR were significantly correlated (r² = 0.602, p = 0.001). Ruptured aneurysms had complex and unstable flow. In ≥7 mm ruptured aneurysms, high aspect ratio, bottleneck ratio, complex flow, unstable flow, low TVR, wall shear stress at aneurysm, high oscillatory shear index, relative resistance time, low wall shear stress area, and ratio of low wall stress area were significant in determining the risk of aneurysm rupture.

Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification

Rupture risk stratification is critical for incidentally detected intracranial aneurysms. Here we developed and validated an institutional nomogram to solve this issue. We reviewed the imaging and clinical databases for aneurysms from January 2015 to September 2018. Aneurysms were reconstructed and morphological features were extracted by the Pyradiomics in python. Multiple logistic regression was performed to develop the nomogram. The consistency of the nomogram predicted rupture risks and PHASES scores was assessed. The performance of the nomogram was evaluated by the discrimination, calibration, and decision curve analysis (DCA). 719 aneurysms were enrolled in this study. For each aneurysm, twelve morphological and nine clinical features were obtained. After logistic regression, seven features were enrolled in the nomogram, which were SurfaceVolumeRatio, Flatness, Age, Hyperlipemia, Smoker, Multiple aneurysms, and Location of the aneurysm. The nomogram had a positive and close correlation with PHASES score in predicting aneurysm rupture risks. AUCs of the nomogram in discriminating aneurysm rupture status was 0.837 in a separate testing set. The calibration curves fitted well and DCA demonstrated positive net benefits of the nomogram in guiding clinical decisions. In conclusion, Pyradiomics derived morphological features based institutional nomogram was useful for aneurysm rupture risk stratification.

Sensitivity of the Unruptured Intracranial Aneurysm Treatment Score (UIATS) in the Elderly: Retrospective Analysis of Ruptured Aneurysms

BACKGROUND/PURPOSE

The prevalence of intracranial aneurysms, as well as the incidence of subarachnoid hemorrhage (SAH), increase with age, and the elderly have poor outcomes after SAH. Age is a key factor in the unruptured intracranial aneurysm treatment score (UIATS),but the sensitivity of the UIATS model in detecting risk of SAH among the elderly is unknown.

METHODS

We retrospectively analyzed 153 consecutive cases of ruptured aneurysms between 2012 and 2018. We used Fisher's exact test, analysis of variance, and multivariate logistic regression to compare outcomes between those >65 years of age and those younger. We then applied the UIATS model and evaluated the sensitivity of the model as a predictor of SAH in the elderly compared with younger patients.

RESULTS

Elderly patients made up 32% (n = 49 of 153) of our cohort. They had significantly higher in-hospital mortality (19 of 49, 39%) than younger patients (14 of 104, 13%) (P < 0.01). In a multivariate logistic regression, controlling for Hunt-Hess grade and comorbidities, age >65 years remained a significant predictor of unfavorable outcome at discharge (P = 0.03). The UIATS model had low sensitivity in the elderly compared with younger patients: 63% (59 of 136) of younger patients would have been recommended aneurysm repair had their aneurysm been detected unruptured, compared with only 12% (5 of 42) of elderly patients >65 years (P < 0.01).

CONCLUSIONS

Elderly patients >65 years in age have far worse outcomes after SAH. The sensitivity of the UIATS model for detecting those at risk of SAH was significantly lower in elderly patients. The UIATS model may lead to undertreatment of elderly patients at risk of SAH.

Management of Unruptured Intracranial Aneurysms

Unruptured intracranial aneurysms (UIAs) are common and are being detected with increasing frequency given the improved quality and higher frequency of cross-sectional imaging. The long-term natural history of UIAs remains poorly understood. To date, there is relative lack of clear guidelines for selection of patients with UIAs for treatment. Surveillance imaging for untreated UIAs is frequently performed, but frequency, duration, and modality of surveillance imaging need clearer guidelines. The authors review the current evidence on prevalence, natural history, role of treatment, and surveillance and screening imaging and highlight the areas for further research.

Application of unruptured aneurysm scoring systems to a cohort of ruptured aneurysms: are we underestimating rupture risk?

The predictive values of current risk stratification scales such as the Unruptured Intracranial Aneurysm Treatment Score (UIATS) and the PHASES score are debatable. We evaluated these scores using a cohort of ruptured intracranial aneurysms to simulate their management recommendations had the exact same patients presented prior to rupture. A prospectively maintained database of ruptured saccular aneurysm patients presenting to our institution was used. The PHASES score was calculated for 992 consecutive patients presenting between January 2002 and December 2018, and the UIATS was calculated for 266 consecutive patients presenting between January 2013 and December 2018. A shorter period was selected for the UIATS cohort given the larger number of variables required for calculation. Clinical outcomes were compared between UIATS-recommended "observation" aneurysms and all other aneurysms. Out of 992 ruptured aneurysms, 54% had a low PHASES score (≤5). Out of the 266 ruptured aneurysms, UIATS recommendations were as follows: 68 (26%) "observation," 97 (36%) "treatment," and 101 (38%) "non-definitive." The UIATS conservative group of patients developed more SAH-related complications (78% vs. 65%, p=0.043), had a higher rate of non-home discharge (74% vs. 46%, p<0.001), and had a greater incidence of poor functional status (modified Rankin scale >2) after 12-18 months (68% vs. 51%, p=0.014). Current predictive scoring systems for unruptured aneurysms may underestimate future rupture risk and lead to more conservative management strategies in some patients. Patients that would have been recommended for conservative therapy were more likely to have a worse outcome after rupture.

Multidimensional predicting model of intracranial aneurysm stability with backpropagation neural network: a preliminary study

OBJECTIVES

The stability of intracranial aneurysms (IAs) may involve in multidimensional factors. Backpropagation (BP) neural network could be adopted to support clinical work. This preliminary study aimed to delve into the feasibility of BP neural network in assessing the risk of IA rupture/growth and to prove the advantage of multidimensional model over single/double-dimensional model.

METHODS

Thirty-six IA patients were recruited from a prospective registration study (ChiCTR1900024547). All patients were followed up until aneurysm ruptured/grew or 36 months after being diagnosed with the IAs. The multidimensional data regarding clinical, morphological, and hemodynamic characteristics were acquired. Hemodynamic analyses were conducted with patient-specific models. Based on these characteristics, seven models were built with BP neural network (the ratio of training set to validation set as 8:1). The area under curves (AUC) was calculated for subsequent comparison.

RESULTS

Forty-five characteristics were determined from 36 patients with 37 IAs. In the models based on the single dimension of IA characteristics, only morphological characteristics exhibited high performance in assessing 3-year IA stability (AUC = 0.703, P = 0.035). Among the models integrating two dimensions of IA characteristics, clinical-morphological (AUC = 0.731, P = 0.016), clinical-hemodynamic (AUC = 0.702, P = 0.036), and morphological-hemodynamic (AUC = 0.785, P = 0.003) models were capable of assessing the risk of 3-year IA rupture/growth. Moreover, the models including all three dimensions exhibited the maximum predicting significance (AUC = 0.811, P = 0.001).

CONCLUSION

The present preliminary study reported that BP neural network might support assessing the 3-year stability of IAs. Models based on multidimensional characteristics could improve the assessment accuracy for IA rupture/growth.

Lower risk of subarachnoid haemorrhage in diabetes: a nationwide population-based cohort study

Background and purpose

Diabetes mellitus (DM) is a common metabolic disorder with increased risk of cardiovascular and cerebrovascular complications. However, its relationship with risk of subarachnoid haemorrhage (SAH), the most devastating form of stroke, remains controversial.

Methods

To evaluate the relationship between DM and risk of SAH, we performed a retrospective cohort study using a nationwide, population‐based, health screening database in Korea. We included participants without history of stroke who underwent a nationwide health screening programme between 2003 and 2004. Primary outcome was occurrence of SAH. Participants were followed up until development of SAH or December 2015. Multivariate Cox proportional hazards regression analysis was performed with adjustments for age, sex, systolic blood pressure, total cholesterol, body mass index, physical activity, smoking status, alcohol habit, household income and treatment with antihypertensive agents and statins.

Results

Among 421 768 study participants, prevalence of DM was 9.6%. During a mean follow-up period of 11.6±1.9 years, 1039 patients developed SAH. Presence of DM was significantly associated with decreased risk of SAH (adjusted HR 0.68; 95% CI 0.53 to 0.86; p<0.001). Elevated level of fasting blood glucose was also negatively associated with risk of SAH (adjusted HR per 1 mmol/L increase 0.90; 95% CI 0.86 to 0.95; p<0.001).

Conclusion

DM and elevated level of fasting blood glucose were inversely associated with risk of SAH. Further studies may elucidate the possibly protective, pathophysiological role played by hyperglycaemia in patients at risk of SAH.

Morphological variables associated with ruptured basilar tip aneurysms

Morphological factors of intracranial aneurysms and the surrounding vasculature could affect aneurysm rupture risk in a location specific manner. Our goal was to identify image-based morphological parameters that correlated with ruptured basilar tip aneurysms. Three-dimensional morphological parameters obtained from CT-angiography (CTA) or digital subtraction angiography (DSA) from 200 patients with basilar tip aneurysms diagnosed at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016 were evaluated. We examined aneurysm wall irregularity, the presence of daughter domes, hypoplastic, aplastic or fetal PCoAs, vertebral dominance, maximum height, perpendicular height, width, neck diameter, aspect and size ratio, height/width ratio, and diameters and angles of surrounding parent and daughter vessels. Univariable and multivariable statistical analyses were performed to determine statistical significance. In multivariable analysis, presence of a daughter dome, aspect ratio, and larger flow angle were significantly associated with rupture status. We also introduced two new variables, diameter size ratio and parent-daughter angle ratio, which were both significantly inversely associated with ruptured basilar tip aneurysms. Notably, multivariable analyses also showed that larger diameter size ratio was associated with higher Hunt-Hess score while smaller flow angle was associated with higher Fisher grade. These easily measurable parameters, including a new parameter that is unlikely to be affected by the formation of the aneurysm, could aid in screening strategies in high-risk patients with basilar tip aneurysms. One should note, however, that the changes in parameters related to aneurysm morphology may be secondary to aneurysm rupture rather than causal.

Irregular pulsation of intracranial unruptured aneurysm detected by four-dimensional CT angiography is associated with increased estimated rupture risk and conventional risk factors

BACKGROUND

Intracranial aneurysms (IAs) are common in the population and current imaging-based rupture risk assessment needs to be refined. We aimed to use four-dimensional CT angiography (4D-CTA) to investigate the associations of irregular pulsation of IAs with conventional risk factors and the estimated rupture risk.

METHODS

One hundred and five patients with 117 asymptomatic IAs underwent 4D-CTA. Geometric and morphologic parameters were measured and the presence of irregular pulsation (defined as a temporary focal protuberance ≥1 mm on more than three successive frames) was identified on 4D-CTA movies. One- and 5 year aneurysm rupture risk were estimated using UCAS and PHASES calculators. Univariate and multivariate analyses were performed to investigate the conventional risk factors associated with irregular pulsation.

RESULTS

Irregular pulsation was observed in 41.0% (48/117) of IAs. Aneurysm size (OR=1.380, 95% CI 1.165 to 1.634), irregular shape (OR=3.737, 95% CI 1.108 to 12.608), and internal carotid artery location (OR=0.151, 95% CI 0.056 to 0.403) were independently associated with irregular pulsation (P<0.05). Aneurysms with irregular pulsation had more than a 6-fold higher estimated rupture risk (1- and 5-year risk [95% CI], 1.56% [0.42%-3.91%], and 2.40% [1.30%-4.30%], respectively) than aneurysms without irregular pulsation (0.23% [0.14%-0.78%] and 0.40% [0.40%-1.30%], respectively) (P<0.001).

CONCLUSIONS

IAs with irregular pulsation are associated with larger size, irregular-shape, and non-ICA origin, and have more than a 6-fold higher estimated 1- and 5-year rupture risk than aneurysms without irregular pulsation. Irregular pulsation should be validated in future longitudinal studies to determine its predictive value for aneurysm growth and rupture.

Risk of Rupture of Small Intracranial Aneurysms (≤5 mm) Among the Chinese Population

OBJECTIVE

We sought to develop a model to predict the risk of small intracranial aneurysm (SIA; ≤5 mm) rupture among Chinese adults and to compare the score predicted by our model with the PHASES (population, hypertension, age, size, earlier subarachnoid hemorrhage, aneurysm site) score.

METHODS

From August 2011 to June 2015, 366 patients with 394 SIAs were retrospectively evaluated and followed up for ≥5 years. The clinical characteristics of the patients were reviewed from their medical records, and the SIA features were evaluated from the imaging studies. The independent risk factors for SIA rupture were studied using multiple Cox proportional hazards regression analysis. The diagnostic value of the PHASES score for the prediction of SIA rupture was also calculated.

RESULTS

Six SIAs in 6 different patients had ruptured during a mean follow-up of 6.4 years. An irregular shape (odds ratio [OR], 31.464), a high aspect ratio (OR, 40.573), and a high size ratio (OR, 20.541) increased the risk of rupture. The predictive score incorporated these three factors. The threshold was 1.5, and the area under the curve, sensitivity, and specificity were 0.986, 100%, and 94.6%, respectively. For the PHASES score, the area under the curve, sensitivity, and specificity were 0.702, 83.3%, and 62.1%, respectively.

CONCLUSIONS

An irregular shape, a high aspect ratio, and a high size ratio were associated with SIA rupture in the Chinese population. Our predictive score is of great value in predicting the risk of SIA rupture.

Retrospective Application of UIATS Recommendations to a Multicenter Cohort of Ruptured Intracranial Aneurysms: How It Would Have Oriented the Treatment Choices?

BACKGROUND

Decisions to treat unruptured intracranial aneurysms remain challenging, as the risk of rupture needs to be balanced with risk of intervention. In 2015, the Unruptured Intracranial Aneurysm Treatment Score (UIATS) was introduced to assist physicians in the decision making process. As its reliability is still debated, we retrospectively applied UIATS to a multicenter cohort of aneurysmal subarachnoid hemorrhage patients to test its performance in suggesting treatment in patients with known natural history.

METHODS

Demographical, clinical, and radiological data of subarachnoid patients admitted in 2 referral cerebrovascular centers were collected. UIATS was individually calculated for each patient, and resulting recommendations were pooled in 3 groups (favor treatment, favor conservative management, unclear indication).

RESULTS

One-hundred and forty-six patients were included in this study. In the event of aneurysm discovery previous to their rupture, UIATS calculation revealed that 40 (27.4%) patients would have received an indication-to-treat advice (mean score: 4.58 ± 1.32), 46 (31.5%) would have been suggested a conservative management (mean score: -7.07 ± 4.15), and 60 (41.1%) would have been included in an unclear recommendation group (mean score: 0.07 ± 1.42). UIATS sensitivity ranged between 27% and 68% depending on the inclusion of patients with unclear indication within the conservative or intervention group.

CONCLUSIONS

In our study, UIATS would have failed to provide a clear recommendation to treat in up to 72.6% of patients whose aneurysm eventually ruptured. In agreement with previous reports, we provide additional evidence that some unruptured intracranial aneurysms may elude UIATS sensitivity. Further long-term prospective studies are necessary to assess UIATS reliability in real-world clinical practice.

Impact of smoking on course and outcome of aneurysmal subarachnoid hemorrhage

Background

While the smoking-related risk of experiencing an aneurysmal subarachnoid hemorrhage (aSAH) is well established, it remains unclear whether smoking has an unexpected “protective effect” in aSAH, or if smokers are more at risk for complications and poor outcomes.

Methods

Prospective, observational study investigating the course and outcome of aSAH in patients admitted during the years 2011 and 2012. Smoking status at admittance, demographic, medical, and radiological variables were registered along with management, complications, and outcome at 1 year in terms of mortality, modified Rankin score, and Glasgow outcome score extended. We compared current smokers with nonsmokers on group level and by paired analysis matched by aSAH severity, age, and severity of vasospasm.

Results

We included 237 patients, thereof 138 current smokers (58.2%). Seventy-four smoker/nonsmoker pairs were matched. Smokers presented more often in poor clinical grade, had less subarachnoid blood, and were younger than nonsmokers. Ruptured aneurysms were larger, and multiple aneurysms more common in smokers. Severe multi-vessel vasospasm was less frequent in smokers, whereas all other complications occurred at similar rates. Mortality at 30 days was lower in smokers and functional outcome was similar in smokers and nonsmokers. Poor clinical grade, age, cerebral infarction, and vertebrobasilar aneurysms were independent predictors of 1-year mortality and of poor functional outcome. Serious comorbidity was a predictor of 1-year mortality. Smoking did not predict mortality or poor functional outcome.

Conclusions

Notwithstanding clinically more severe aSAH, smokers developed less frequently severe vasospasm and had better outcome than expected. The risk for complications after aSAH is not increased in smokers.

Intracranial Hemorrhage From Dural Arteriovenous Fistulas: Symptoms, Early Rebleed, and Acute Management: A Single-Center 8-Year Experience

BACKGROUND

Cerebral dural arteriovenous fistulas (dAVFs) presenting with hemorrhage are so rare that reports on their characteristics and guidelines for their acute management are scarce.

OBJECTIVE

To identify characteristics of the clinical and radiological presentation of hemorrhaging dAVFs, and establish their frequency of early rebleed so that implications for their acute management can be drawn.

METHODS

Retrospective analysis of all patients admitted with intracranial hemorrhage from a dAVF during the years 2011 to 2018.

RESULTS

Twenty patients (14 males) with a median age of 61 yr (27-75 yr) were included. Thunderclap headache was the presenting symptom in 13 (65%) patients. Rebleed prior to treatment occurred in 35% of the patients at median 7.5 h (range 3-96 h) after the ictus. All dAVFs had retrograde venous drainage and a venous aneurysm with a bleb was the source of hemorrhage in 16 (80%) patients, all of them presenting with headache. In contrast, patients bleeding due to diffuse venous hypertension presented with neurological deficits. Endovascular treatment was successful in 2 cases; hence, definite dAVF treatment was surgical in 18 (90%) patients. At median 7 mo (2-29 mo) after the ictus, 13 (65%) patients were in Glasgow Outcome Scale-Extended 7 or 8.

CONCLUSION

The typical presentation of hemorrhage from a cranial dAVF is thunderclap headache. The origin of hemorrhage is often a ruptured venous aneurysm with a bleb. The high frequency of early rebleeds warrants management strategies equivalent to those established for aneurysmal subarachnoid hemorrhage. Overall outcome is favorable.

A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms

OBJECTIVES

Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture.

METHODS

One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use.

RESULTS

Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876.

CONCLUSIONS

Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance.

KEY POINTS

• Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms.

Small Intracranial Aneurysms and Subarachnoid Hemorrhage: Is the Size Criterion for Risk of Rupture Relevant?

The current understanding is that small intracranial aneurysms (<7 mm) are not at a significant risk for rupture. However, there have been several published series of rupture and subarachnoid hemorrhage from aneurysms <5 mm. Three cases of intracranial aneurysms rupturing at <3 mm are presented in this paper. Patient age ranged between 38 and 57 years. The aneurysms were located in different parts of the circulation in the brain. This case series highlights that the size criterion alone is not adequate when evaluating patients with unruptured brain aneurysms for observational follow-up or treatment.

Role of Morphological and Hemodynamic Factors in Predicting Intracranial Aneurysm Rupture: A Review

Intracranial aneurysms (IAs) carry the risk of rupture, which will lead to subarachnoid hemorrhage, which has a high mortality and morbidity risk. However, the treatment of IA's carries mortality and morbidity risks too. There are well-known risk factors for the rupture of IAs like age, size, and site. However, choosing patients with unruptured IAs for treatment is still a big challenge. This review article aimed to find out the relationship between morphological and hemodynamic characters of IAs with their rupture and incorporate these factors with well-known factors to yield an accurate module for predicting the rupture of IAs and decision-making in the treatment of unruptured IAs.

We searched in PubMed and Medline databases by using the following keywords: IAs, subarachnoid hemorrhage, and risk of rupture, morphology, and hemodynamic “mesh.” A total of 19 studies with 7269 patients and 9167 IAs, of which 1701 had ruptured, were reviewed thoroughly. Some modules like population, hypertension, age, size, earlier subarachnoid hemorrhage, and site (PHASES) score that involve well-known risk factors can be used to assess the risk of rupture of IAs. However, decision making for treating unruptured IA needs more detailed and more accurate modules. Studying morphological and hemodynamic factors and incorporation of them with well-known risk factors to yield a more comprehensive module will be very helpful in treating unruptured IA. Among morphological factors, aspect ratio (AR), size ratio (SR), aneurysm height, and bottle-neck factor showed significant effects on the growth and rupture of IA. Besides, wall shear stress (WSS), oscillatory shear index (OSI), and low wall shear stress area (LSA) as hemodynamic factors could have a substantial impact on the formation, shape, growth, and rupture of unruptured IA.