Morphological parameters and anatomical locations associated with rupture status of small intracranial aneurysms

Can the radiomics features of intracranial aneurysms predict the prognosis of aneurysmal subarachnoid hemorrhage?

Objectives

This study attempted to determine potential predictors among radiomics features for poor prognosis in aneurysmal subarachnoid hemorrhage (aSAH), develop models for prediction, and verify their predictive power.

Methods

In total, 252 patients with aSAH were included in this study and categorized into favorable and poor outcome groups based on the modified Rankin Scale score 3 months after event. Radiomics features of the ruptured intracranial aneurysm extracted from computed tomography angiography images were selected using least absolute shrinkage and selection operator regression and 10-fold cross-validation. A radiomics score was created by selecting the optimal features. Other risk factors for a poor prognosis were screened using multivariate regression analysis. Three models (clinical, aneurysm, and clinical-aneurysm combined models) were developed. The performance of the models was assessed using receiver operating characteristic (ROC) curves. A clinical-aneurysm combined nomogram was constructed to forecast the risk of poor prognosis in patients with aSAH.

Results

A total of three clinical variables and six radiomics features were shown to have a significant association with poor prognosis in patients with aSAH. In the training cohort, the clinical, aneurysm, and clinical-aneurysm combined models had areas under the ROC curves of 0.846, 0.762, and 0.893, respectively. In the testing cohort, these models had areas under the ROC curves of 0.848, 0.753, and 0.869, respectively.

Conclusion

The radiomics characteristics of ruptured intracranial aneurysms are valuable to predict prognosis after aSAH. The clinical-aneurysm combined model exhibited the best among the three models. The clinical-aneurysm combined nomogram is a reliable and effective tool for predicting poor prognosis in patients with aSAH.

Development and Validation of a Risk Predictive Model for Small Intracranial Aneurysms in Adults Over a Five-Year Period

Objective The optimal management of a small intracranial aneurysm (sIA) remains a challenge due to the lack of a size-specific risk predictive model for aneurysm rupture. We aimed to develop and validate a nomogram-based risk predictive model for sIA. Methods A total of 382 patients harboring 215 ruptured and 167 unruptured small intracranial aneurysms (uSIAs) (≤ 7 mm) were recruited and divided into training and validation cohorts. Risk factors for the construction of a nomogram were selected from clinical and aneurysmal features by least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. The nomogram for risk of rupture was evaluated in both the training and validation cohorts for discrimination, calibration, and clinical usefulness. Results Hyperlipidemia (odds ratio (OR)=2.74, 95% confidence interval (CI)=1.322~5.956, P=0.008), the presence of a daughter dome (OR=3.068, 95%CI=1.311~7.598, P=0.012), larger size-to-neck ratio (SN) (OR=1.807, 95%CI=1.131~3.063, P=0.021) and size ratio (SR) (OR=2.221, 95%CI=1.262~4.025, P=0.007) were selected as independent risk factors for sIA rupture and used for construction of nomogram. Internal validation by bootstrap sampling showed the Concordance index (C index) of 0.756 for the nomogram. The calibration by the Hosmer-Lemeshow test showed a P value of 0.847, indicating the model was well-fitted. Additionally, decision curve analysis (DCA) demonstrated that the predictive model has good clinical usefulness, providing net benefits across a range of threshold probabilities, thus supporting its application in clinical decision-making. Conclusion The risk prediction model can reliably predict the risk of sIA rupture, which may provide an important reference for optimizing the therapeutic strategy.

A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning

Cerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatment becomes evident upon diagnosis, as the severity of the disease guides the course of action. Cerebral aneurysms are particularly vulnerable in the circle of Willis and pose a significant concern due to the potential for rupture, which can lead to irreversible consequences, including fatality. The primary objective of this study is to predict the rupture status of cerebral aneurysms. To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. Diagnosing and predicting aneurysm rupture based solely on brain scans is a significant challenge with limited reliability, even for experienced physicians. However, by employing statistical methods and machine learning techniques, we can assist physicians in making more confident predictions regarding rupture likelihood and selecting appropriate treatment strategies. To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. The dataset comprised 3 clinical features and 35 morphological parameters, including 8 novel morphological features introduced for the first time in this study. Our models demonstrated exceptional performance in predicting cerebral aneurysm rupture, with accuracy ranging from 0.76 to 0.82 and precision score from 0.79 to 0.83 for the test dataset. As the data are sensitive and the condition is critical, recall is prioritized as the more crucial parameter over accuracy and precision, and our models achieved outstanding recall score ranging from 0.85 to 0.92. Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. The ellipticity index, size ratio, and shape irregularity are pivotal features in predicting aneurysm rupture, respectively, contributing significantly to our understanding of this complex condition. Among the multitude of parameters under investigation, these are particularly important. In this study, the ideal roundness parameter was introduced as a novel consideration and ranked fifth among all 38 parameters. Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.

Investigation of the morphometric characteristics of internal carotid artery between sexes and in patients with intracranial aneurysms

PURPOSE

The purpose of this study is to investigate the morphometric properties of the internal carotid artery (ICA) by measuring the diameters and angles of its segments and exploring variations related to sex and the presence of aneurysms.

METHODS

Digital subtraction angiography (DSA) images were utilized from 130 aneurysm patients and 75 non-aneurysm individuals to create 3D ICA models using 3D Slicer software. Segment diameters were measured via Autodesk Meshmixer 3.5.474 and angles were evaluated using ImageJ software.

RESULTS

In total, DSA images of 130 aneurysm patients and 75 individuals with normally reported carotid systems were evaluated. It was found that the intracranial aneurysms (IAs) were predominantly formed on the anterior cerebral artery (ACA) in males (%43), whereas in females IAs were frequently localized in the C6 segment (31.7%) and middle cerebral artery (MCA) (30.2%). In the control group, the evaluation of gender differences in segment diameters and angles revealed that males had significantly larger C4 and C5 segment diameters (4.62 vs. 4.32 mm and 4.41 vs. 4.09 mm, respectively) and a greater C6 angle (146.9° vs. 139.7°) compared to females. Comparisons between patients with an aneurysm at the anterior cerebral artery (ACA) and the control group revealed that the ACA group had wider diameters in the C1 (4.88 vs. 4.53 mm), C3 (4.65 vs. 4.4 mm), C5 (4.51 vs. 4.25 mm), and ACA (2.36 vs. 2.06 mm) segments. Additionally, the ACA group had wider angles in the ACA (104.1° vs. 94.1°) and C6 segments (147.7° vs. 143.3°), whereas the control group exhibited wider angles in the middle cerebral artery (MCA) segment (141.5° vs. 135.5°) compared to the ACA aneurysm group. Patients with anterior cerebral artery (ACA) aneurysms exhibited larger diameters in C1, C3, C5, C6, and ACA segments compared to the control group. Additionally, while the control group had larger MCA angle, patients with ACA aneurysms had larger angles in C6 segment and ACA.

CONCLUSION

Our results demonstrated that formation of aneurysms is affected by anatomical configuration of the ICA as well as sex characteristics, particularly regarding the ACA and MCA bifurcation angles, which showed associations with aneurysms in the respective branches.

Time-of-Flight MRA of Intracranial Aneurysms with Interval Surveillance, Clinical Segmentation and Annotations

Intracranial aneurysms (IAs) are present in 2-6% of the global population and can be catastrophic upon rupture with a mortality rate of 30-50%. IAs are commonly detected through time-of-flight magnetic resonance angiography (TOF-MRA), however, this data is rarely available for research and training purposes. The provision of imaging resources such as TOF-MRA images is imperative to develop new strategies for IA detection, rupture prediction, and surgical training. To support efforts in addressing data availability bottlenecks, we provide an open-access TOF-MRA dataset comprising 63 patients, of which 24 underwent interval surveillance imaging by TOF-MRA. Patient scans were evaluated by a neuroradiologist, providing aneurysm and vessel segmentations, clinical annotations, 3D models, in addition to 3D Slicer software environments containing all this data for each patient. This dataset is the first to provide interval surveillance imaging for supporting the understanding of IA growth and stability. This dataset will support computational and experimental research into IA dynamics and assist surgical and radiology training in IA treatment.

Prediction of small intracranial aneurysm rupture status based on combined Clinical-Radiomics model

Background

Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors.

Methods

A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis.

Results

A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model.

Conclusion

Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.

Towards the mechanical characterisation of unruptured intracranial aneurysms: Numerical modelling of interactions between a deformation device and the aneurysm wall

Intracranial aneurysm is a critical pathology related to the arterial wall deterioration. This work is an essential aspect of a large scale project aimed at providing clinicians with a non-invasive patient-specific decision support tool regarding the rupture risk assessment. A machine learning algorithm links the aneurysm shape observed and a database of UIA clinical images associated with in vivo wall mechanical properties and rupture characterisation. The database constitution is derived from a device prototype coupled with medical imaging. It provides the mechanical characterisation of the aneurysm from the wall deformation obtained by inverse analysis based on the variation of luminal volume. Before performing in vivo tests of the device on small animals, a numerical model was built to quantify the device's impact on the aneurysm wall under natural blood flow conditions. As the clinician will never be able to precisely situate the device, several locations were considered. In preparation for the inverse analysis procedure, artery material laws of increasing complexity were studied (linear elastic, hyper elastic Fung-like). Considering all the device locations and material laws, the device induced relative displacements to the Systole peak (worst case scenario with the highest mechanical stimulus linked to the blood flow) ranging from 375 μm to 1.28 mm. The variation of luminal volume associated with the displacements was between 0.95 % and 4.3 % compared to the initial Systole volume of the aneurysm. Significant increase of the relative displacements and volume variations were found with the study of different cardiac cycle moments between the blood flow alone and the device application. For forthcoming animal model studies, Spectral Photon CT Counting, with a minimum spatial resolution of 250 μm, was selected as the clinical imaging technique. Based on this preliminary study, the displacements and associated volume variations (baseline for inverse analyse), should be observable and exploitable.

Evaluation of PHASES Score for Predicting Rupture of Intracranial Aneurysms: Significance of Aneurysm Size

BACKGROUND

Recent data have identified that certain risk factors for rupture differ between small and larger intracranial aneurysms (IAs). Such differing risk factors make up 5 out of the 6 predictor variables used in the PHASES score, which raises the question on whether IA size has a significant effect on the score's performance.

METHODS

Patients who were diagnosed with an IA incidentally or due to a subarachnoid hemorrhage between 2015 and 2023 were selected for potential inclusion. The median IA size of the cohort was chosen as the cutoff point to categorize small and large (6 mm). The PHASES score was calculated for all patients, and a receiver operating characteristic curve analysis was performed to evaluate the classification accuracy of PHASES in predicting rupture for small and large IAs.

RESULTS

A total of 677 IAs were included. Among the IAs, 400 (58.9%) presented as UIAs and 279 (41.0%) as subarachnoid hemorrhage. The average PHASES score was 2.9 and 6.5 for small (n = 322) and large (n = 355) IAs, respectively. The PHASES score performed significantly lower for predicting rupture in smaller IAs (area under the curve: 0.634) compared with the larger (area under the curve: 0.741) (P = 0.00083).

CONCLUSIONS

PHASES was shown to underperform on small IAs. The decision to treat small unruptured IAs remains highly controversial, and the development of a new score to estimate the annual rupture rate while accounting for IA morphology is of great need. Our findings can help encourage future researchers to develop such a score.

Rupture Risk Factors and Strategies for Unruptured Distal Anterior Cerebral Artery Aneurysms

BACKGROUND

Distal anterior cerebral artery (dACA) aneurysms are rare. Ruptured dACA aneurysms typically present with subarachnoid hemorrhage in conjunction with intracerebral hematoma and cause neurological deterioration. This study aimed to determine their risk of rupture and examine associated factors.

METHODS

We retrospectively analyzed patients with dACA aneurysms to compare patient and aneurysm characteristics between ruptured and unruptured aneurysms. Clinical outcome was used the modified Rankin scale. Univariate analyses were performed to identify rupture risk factors.

RESULTS

One hundred three patients with dACA aneurysms were examined (51 ruptured and 52 unruptured). The median aspect ratio of ruptured and unruptured aneurysms was 1.69 and 1.22, respectively (P < 0.01). The median maximum diameter of ruptured and unruptured aneurysms was 5.2 and 3.1 mm, respectively (P < 0.01). The median size ratio of ruptured and unruptured aneurysms was 3.32 and 2.17, respectively (P < 0.01). Maximum diameter was <5 mm in 45.2% of ruptured dACA aneurysms. dACA aneurysm, showing size ratio >2.4 and aspect ratio >1.4, had ruptured in 71.4% and 78.6%, respectively. We suggested that these are the threshold of size ratio and aspect ratio for rupture of dACA aneurysms. A total percentatge of 78.1% of aneurysms with aspect ratio >1.4 and size ratio >2.4 had ruptured.

CONCLUSIONS

Distal anterior cerebral artery (dACA) aneurysms may rupture, even when small. We found a significant difference between ruptured and unruptured aneurysms with respect to maximum diameter, aspect ratio, and size ratio. Treatment for small aneurysms should be considered based on size ratio and aspect ratio, not just size.

Predictors for Rupture of Small (<7mm) Intracranial Aneurysms: A Systematic Review and Meta-Analysis

INTRODUCTION

Identifying predictors for rupture of small intracranial aneurysms (sIAs) have become a growing topic in the literature given the relative paucity of data on their natural history. The authors performed a meta-analysis to identify reliable predictors.

METHODS

PubMed, Scopus, and Web of Science were used to systematically extract references which involved at least 10 IAs <7mm which including a control group experiencing no rupture. All potential predictors reported in the literature were evaluated in the meta-analysis.

RESULTS

Fifteen studies yielding 4,739 sIAs were included in the meta-analysis. Four studies were prospective and 11 were retrospective. Univariate analysis identified 7 predictors which contradicted or are absent in the current scoring systems, while allowing to perform subgroup analysis for further reliability: patient age (MD -1.97, 95%CI -3.47-0.48; P = 0.01), the size ratio (MD 0.40, 95%CI 0.26-0.53; P < 0.00001), the aspect ratio (MD 0.16, 95%CI 0.11-0.22; P < 0.00001), bifurcation point (OR 3.76, 95%CI 2.41-5.85; P < 0.00001), irregularity (OR 2.95, 95%CI 1.91-4.55; P < 0.00001), the pressure loss coefficient (MD -0.32, 95%CI -0.52-0.11; P = 0.002), wall sheer stress (Pa) (MD -0.16, 95%CI -0.28-0.03; P = 0.01). All morphology related predictors listed above have been confirmed as independent predictors via multivariable analysis among the individual studies.

CONCLUSIONS

Morphology related predictors are superior to the classic patient demographic predictors present in most scoring systems. Given that morphology predictors take time to measure, our findings may be of great interest to developers seeking to incorporate artificial intelligence into the treatment decision-making process.

New morphological parameter for intracranial aneurysms and rupture risk prediction based on artificial neural networks

BACKGROUND

Numerous studies have evaluated the rupture risk of intracranial aneurysms using morphological parameters because of their good predictive capacity. However, the limitation of current morphological parameters is that they do not always allow evaluation of irregularities of intracranial aneurysms. The purpose of this study is to propose a new morphological parameter that can quantitatively describe irregularities of intracranial aneurysms and to evaluate its performance regarding rupture risk prediction.

METHODS

In a retrospective study, conventional morphological parameters (aspect ratio, bottleneck ratio, height-to-width ratio, volume to ostium ratio, and size ratio) and a newly proposed morphological parameter (mass moment of inertia) were calculated for 125 intracranial aneurysms (80 unruptured and 45 ruptured aneurysms). Additionally, hemodynamic parameters (wall shear stress and strain) were calculated using computational fluid dynamics and fluid-structure interaction. Artificial neural networks trained with each parameter were used for rupture risk prediction.

RESULTS

All components of the mass moment of inertia (Ixx, Iyy, and Izz) were significantly higher in ruptured cases than in unruptured cases (p values for Ixx, Iyy, and Izz were 0.032, 0.047, and 0.039, respectively). When the conventional morphological and hemodynamic parameters as well as the mass moment of inertia were considered together, the highest performance for rupture risk prediction was obtained (sensitivity 96.3%; specificity 85.7%; area under the receiver operating characteristic curve 0.921).

CONCLUSIONS

The mass moment of inertia would be a useful parameter for evaluating aneurysm irregularity and hence its risk of rupture. The new approach described here may help clinicians to predict the risk of aneurysm rupture more effectively.

Analysis of independent risk factors for aneurysm rupture based on carotid tortuosity index and morphological parameters of single intracranial aneurysms in anterior circulation

PURPOSE

Our study focused on the risk factors associated with anterior circulation intracranial aneurysm (IA) rupture by examining the carotid artery (CA) tortuosity index (TI) and anterior circulation IA morphological parameters.

METHOD

This study conducted a retrospective analysis of clinical and imaging data from 163 patients with anterior circulation IA diagnosed by head and neck computed tomography angiography (CTA). The patients were categorized into two groups: the ruptured group (57 cases) and the unruptured group (106 cases). CA was categorized based on its location into three segments: the extracranial segment of the internal carotid artery (EICA) TI, the angle of the internal carotid artery (ICA) and the common carotid artery (CCA) TI. Measure the morphological parameters of all IA: IA length neck (L), IA height (H), aneurysm diameter width (D), the ratio of L to the mean diameter of the IA-bearing artery (SR), the ratio of H to D (AR), the angle of flow inflow (FA) and IA angle (AA). The study conducted five types of analysis to determine the risk factors for anterior circulation IA rupture. The first was an univariate analysis of the risk factors. The second was an analysis of the correlation between CA TI and IA morphological parameters. The third used multivariate logistic stepwise regression analysis to analyse independent risk factors for IA rupture. The fourth was to plot ROC curves to build a predictive model for IA rupture and calculate diagnostic thresholds. Finally, a data set from another hospital (78 cases) was used as a validation set to validate the multivariate model.

RESULT

Univariate analysis revealed that there were statistically significant differences (P < 0.05) in gender, EICA TI, location of IA and IA morphological parameters (FA, H, AR, L, SR), which acted as risk factors for anterior circulation IA rupture. The results of Spearman correlation analysis indicate that CCA TI is significantly correlated with SR, H and L (P < 0.05), while EICA TI is significantly correlated with FA and L (P < 0.05). The results of multivariate logistic analysis showed that FA (OR = 1.072, 95%CI = 1.04-1.10, P < 0.001), SR (OR = 4.949, 95%CI = 1.96-12.53, P = 0.001), EICA TI (OR = 1.037, 95%CI = 1.01-1.07, P = 0.003) were independent risk factors for IA rupture. The ROC curve plotting results suggest that the area under the curve (AUC) of FA is 0.860 with a diagnostic threshold of 110.1°; the AUC of SR is 0.786 with a diagnostic threshold of 1.67; the AUC of EICA TI is 0.723 with a diagnostic threshold of 28.845; the AUC of the three combined is 0.903 with a threshold of 0.480. The combined factor diagnostic model is validated according to the validation set, and the results show that the AUC (0.866) of the validation set is not much different from the AUC (0.903) of the multivariate model, and the multivariate model has a better diagnostic effect.

CONCLUSION

In clinical practice, it is important to consider the evaluation of aneurysm rupture in combination with imaging, as FA, SR and ECIA TI are independent risk factors for IA rupture in the anterior circulation. Unlike the IA morphological parameters, EICA TI is an often overlooked extracranial parameter, but is equally important in its power to predict IA rupture. When the EICA TI exceeds 28.845, the IA has the possibility of rupture. Finally, multivariate diagnostic model are of interest when considering rupture of the anterior circulation IA.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study

Background

Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets.

Methods

We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves.

Results

The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets.

Conclusions

The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

The Application of Multiple Magnetic Resonance Scanning Techniques in Evaluating the Stability of Intracranial Aneurysms

Purpose

To evaluate the stability of unruptured intracranial aneurysm (UIA) with high-resolution magnetic resonance imaging of the vessel wall (HR-VWI).

Materials and Methods

A total of 92 UIA patients were enrolled. After MRA, HR-VWI imaging, the reconstruction of volume rendering (VR) and maximum intensity projection (MIP) were performed to observe the location and size of aneurysms, AR value (ratio of aneurysm height to aneurysmal diameter), SR value (ratio of maximum tumor depth to proximal parent artery diameter), and signal intensity were measured.

Results

There were 7 aneurysms with UIA located in the anterior cerebral artery, 31 aneurysms with UIA in the middle cerebral artery, 1 aneurysm with UIA in the posterior cerebral artery, 18 aneurysms with UIA in the anterior communication, 5 aneurysms with UIA in the posterior communication, 34 aneurysms with UIA in the intracranial segment of the internal carotid artery and 3 aneurysms with UIA in the vertebral artery. Among them, 8 patients had more than two multiple aneurysms. The lesion size was 2-38mm (6.3 ± 5.09). There are 46 aneurysms with wall enhancement: the maximum SR value was 7.03 and the minimum 1.2, and the maximum AR value was 7.5 and the minimum 1.0. Fifty-five aneurysms showed no enhancement of the tumor wall. The maximum SR value was 4.55 and the minimum 0.58, and the maximum AR value was 4.0 and the minimum 0.6, respectively. Patients were divided into a stable group and an unstable group according to the aneurysm wall. The enhancement rate, SR value, and AR value in the stable aneurysm group were significantly lower than those in the unstable aneurysm group (P < 0.05).

Conclusion

MRA and HR-VWI can objectively reflect the stability of aneurysms by judging the morphology, SR value, and signal enhancement of UIA, and can provide a certain basis for diagnosis and treatment, which has become routine examination.

A Novel Risk-Predicted Nomogram for Perioperative Ischemic Complications of Endovascular Treatment Among Ruptured Anterior Communicating Artery Aneurysms

OBJECTIVE

To establish and validate a risk prediction model for perioperative ischemic complication (PIC) of endovascular treatment for ruptured anterior communicating artery aneurysms (ACoAAs).

METHODS

The general clinical and morphologic data, operation schemes, and treatment outcomes of patients with ruptured ACoAAs treated with endovascular treatment in our center from January 2010 to January 2021 were retrospectively analyzed and assigned to primary (359 patients) and validation (67 patients) cohorts. A risk-predicted nomogram for PIC was developed through multivariate logistic regression analysis in the primary cohort. The discrimination ability, calibration accuracy, and clinical usefulness of the established PIC prediction model were evaluated and verified based on the receiver operating characteristic curves, calibration curves, and decision curve analysis in the primary and external validation cohorts, respectively.

RESULTS

A total of 426 patients were included, 47 of whom had PIC. The multivariate logistic regression analysis showed that hypertension, Fisher grade, A1 conformation, use of stent-assisted coiling, and aneurysm orientation were independent risk factors for PIC. Then, we developed a simple and easy-to-use nomogram to predict PIC. This nomogram has a good diagnostic performance (area under the curve, 0.773; 95% confidence interval, 0.685-0.862) and calibration accuracy; we then further validated this nomogram by external validation cohort and showed an excellent diagnostic performance and calibration accuracy. Besides, the decision curve analysis confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

A history of hypertension, high preoperative Fisher grade, complete A1 conformation, use of stent-assisted coiling, and aneurysm orientation (pointing upward) are risk factors for PIC for ruptured ACoAAs. This novel nomogram might serve as a potential early warning sign of PIC for ruptured ACoAAs.

Prediction of cerebral aneurysm rupture using a point cloud neural network

OBJECTIVE

Accurate prediction of cerebral aneurysm (CA) rupture is of great significance. We intended to evaluate the accuracy of the point cloud neural network (PC-NN) in predicting CA rupture using MR angiography (MRA) and CT angiography (CTA) data.

METHODS

418 CAs in 411 consecutive patients confirmed by CTA (n=180) or MRA (n=238) in a single hospital were retrospectively analyzed. A PC-NN aneurysm model with/without parent artery involvement was used for CA rupture prediction and compared with ridge regression, support vector machine (SVM) and neural network (NN) models based on radiomics features. Furthermore, the performance of the trained PC-NN and radiomics-based models was prospectively evaluated in 258 CAs of 254 patients from five external centers.

RESULTS

In the internal test data, the area under the curve (AUC) of the PC-NN model trained with parent artery (AUC=0.913) was significantly higher than that of the PC-NN model trained without parent artery (AUC=0.851; p=0.041) and of the ridge regression (AUC=0.803; p=0.019), SVM (AUC=0.788; p=0.013) and NN (AUC=0.805; p=0.023) radiomics-based models. Additionally, the PC-NN model trained with MRA source data achieved a higher prediction accuracy (AUC=0.936) than that trained with CTA source data (AUC=0.824; p=0.043). In external data of prospective cohort patients, the AUC of PC-NN was 0.835, significantly higher than ridge regression (0.692; p<0.001), SVM (0.701; p<0.001) and NN (0.681; p<0.001) models.

CONCLUSION

PC-NNs can achieve more accurate CA rupture prediction than traditional radiomics-based models. Furthermore, the performance of the PC-NN model trained with MRA data was superior to that trained with CTA data.

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management

PURPOSE

Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine.

METHODS

We propose a pipeline for intracranial aneurysm analysis using deep learning-based mesh segmentation, automatic centerline and outlet detection and automatic generation of a semantic vessel graph. We use the semantic vessel graph for morphological analysis and an automatic rupture state classification.

RESULTS

The deep learning-based mesh segmentation can be successfully applied to aneurysm surface meshes. With the subsequent semantic graph extraction, additional morphological parameters can be extracted that take the whole vascular domain into account. The vessels near ruptured aneurysms had a slightly higher average torsion and curvature compared to vessels near unruptured aneurysms. The 3D surface models can be further employed for rupture state classification which achieves an accuracy of 83.3%.

CONCLUSION

The presented pipeline addresses several aspects of current research and can be used for aneurysm analysis with minimal user effort. The semantic graph representation with automatic separation of the aneurysm from the parent vessel is advantageous for morphological and hemodynamical parameter extraction and has great potential for deep learning-based rupture state classification.

Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction becomes a research Frontier. In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework's performance were investigated. Three-dimensional (3D) segmentation-dedicated architectures, including 3D UNet, VNet, and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial computed tomography angiography (CTA) images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the convolutional neural network mentioned above. The performances of three convolutional neural networks were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitative metrics. All the convolutional neural networks showed a non-zero voxel-wise recall (V-Recall) at the case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ± 0.140 (3.5% and 17.3% higher than that of VNet and 3D Res-UNet), as well as an average dice similarity coefficient (DSC) of 0.818 ± 0.100, which was 4.1%, and 11.7% higher than VNet and 3D Res-UNet. Moreover, the average Hausdorff distance (HD) of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3% and 17.3% lower than that of VNet and 3D Res-UNet. The three-dimensional deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +1.4760/-2.3854 mm, an average distance of 0.3480 mm, a standard deviation (STD) of 0.5978 mm, a root mean square (RMS) of 0.7269 mm. In addition, the average segmentation time (AST) of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.

An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs

Background

The rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision-making.

Purpose

We aim to build an integrated model to improve the assessment of the rupture risk of IAs.

Materials and Methods

A total of 148 (39 ruptured and 109 unruptured) IA subjects were retrospectively computed with computational fluid dynamics (CFDs), and the integrated models were proposed by combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms that include random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM), and LightGBM were, respectively, adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic cloud features from the hemodynamic clouds obtained from CFD. Morphological variables and hemodynamic parameters along with the extracted hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared.

Results

Without consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM, and LightGBM was 0.824, 0.759, 0.839, 0.860, and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925, and 0.890, respectively. With the consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926, and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969, and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively.

Conclusion

The integrated model combining ML and DL algorithms could improve the classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.

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.

Rupture of an adjacent cerebral aneurysm following the deployment of a Pipeline embolization device: illustrative case

BACKGROUND

Although the Pipeline embolization device (PED) is effective for intracranial aneurysm treatment, its impact on the surrounding vascular structure is unknown.

OBSERVATIONS

A 71-year-old woman was incidentally found to have a simultaneous large posterior communicating artery aneurysm and an ipsilateral small anterior choroidal artery aneurysm. She underwent flow diversion therapy for both aneurysms with a PED, but the distal shortening of the PED after deployment led to the exposure of the anterior choroidal artery aneurysm. Follow-up angiography revealed complete obliteration of the posterior communicating artery aneurysm, but the anterior choroidal artery aneurysm remained. Three years after the endovascular surgery, the patient experienced a subarachnoid hemorrhage due to the rupture of the anterior choroidal artery aneurysm. Retrospective analysis of angiographic images revealed a change in the vascular geometry surrounding the ruptured aneurysm after PED deployment; this was further accompanied by an increase in the flow velocity inside the aneurysm.

LESSONS

Because PED use might induce the adverse effects on the adjacent uncovered aneurysm by changing the vascular geometry and hemodynamic stress, a cautious therapeutic strategy, such as proper placement of the stent and using a longer and appropriate-sized PED, should be chosen when deploying the PED.

U-Shaped Association of Aspect Ratio and Single Intracranial Aneurysm Rupture in Chinese Patients: A Cross-Sectional Study

Background: Previous studies have analyzed the association of aspect ratio (AR) on the ruptured intracranial aneurysm (IA), but the findings are inconclusive and controversial. Therefore, the study aimed to derive a more detailed estimation of this association between AR and ruptured IA in Chinese IA patients. Methods: The present work was a cross-sectional study. We retrospectively collected 1,588 Chinese patients with a single IA from January 2010 to November 2017. The relationship was examined between AR at diagnosis and ruptured IA. Covariates included data of demographics, morphological parameters, lifestyle habits, clinical features, and comorbidities. Binary logistic regression and two-piecewise linear models were used to analyze independent associations of AR with ruptured IA. Results: The results suggest that the association between AR and IA rupture was U-shaped. In the AR range of 1.08-1.99, the prevalence of IA rupture was 13% lower for each 0.1-unit increment in AR [odds ratio 0.87, 95% confidence interval (CI) 0.80-0.98]. Conversely, for every 0.1-unit increase in AR, the prevalence of IA rupture increased by ~3% (odds ratio 1.03, 95% CI 1.01-1.06) in the AR range of 3.42-4.08. Conclusion: The relationship between AR and ruptured IA was U-shaped, with the negative association at AR of 1.08-1.99 and positive association at AR of 3.42-4.08.

Definitions of intracranial aneurysm size and morphology: A call for standardization

Background

Intracranial aneurysms (IAs) are classified based on size (maximal dome diameter) as well as additional parameters such as neck diameter and dome-to-neck ratio (DNR). The neurosurgical literature includes a wide variety of definitions for both IA size and neck classifications. Standardizing the definitions of IA size and wide-neck classifications would help eliminate inconsistencies and potential misunderstandings of aneurysm morphology and rupture risk.

Methods

We queried the MEDLINE (EBSCO) database using the terms "unruptured IA" and ("small" or "medium" or "large") and filtered based on publication date, language, and scholarly journals. The resulting articles and their references were further screened for eligibility. This identified 286 records, of which 104 were excluded, leaving 182 articles for analysis. The review found several different IA size classifications and neck classifications.

Results

A review of the existing literature describing size and neck classifications revealed 13 size classifications for small aneurysms, four classifications for medium aneurysms, 15 classifications for large aneurysms, and one classification for giant aneurysms. There were also seven different wide-neck classifications found.

Conclusion

It is imperative that a standardization in classification be implemented to help interventionalists make the most informed decisions regarding emerging treatment options as new endovascular technologies and devices are emerging with indications based around these classifications. Based on the database findings, this article recommends standardized quantitative measurement ranges for IA size and neck classifications.

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.

Shedding the Light on the Natural History of Intracranial Aneurysms: An Updated Overview

The exact molecular pathways underlying the multifactorial natural history of intracranial aneurysms (IAs) are still largely unknown, to the point that their understanding represents an imperative challenge in neurovascular research. Wall shear stress (WSS) promotes the genesis of IAs through an endothelial dysfunction causing an inflammatory cascade, vessel remodeling, phenotypic switching of the smooth muscle cells, and myointimal hyperplasia. Aneurysm growth is supported by endothelial oxidative stress and inflammatory mediators, whereas low and high WSS determine the rupture in sidewall and endwall IAs, respectively. Angioarchitecture, age older than 60 years, female gender, hypertension, cigarette smoking, alcohol abuse, and hypercholesterolemia also contribute to growth and rupture. The improvements of aneurysm wall imaging techniques and the implementation of target therapies targeted against inflammatory cascade may contribute to significantly modify the natural history of IAs. This narrative review strives to summarize the recent advances in the comprehension of the mechanisms underlying the genesis, growth, and rupture of IAs.

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.

VICTORIA: VIrtual neck Curve and True Ostium Reconstruction of Intracranial Aneurysms

Purpose

For the status evaluation of intracranial aneurysms (IAs), morphological and hemodynamic parameters can provide valuable information. For their extraction, a separation of the aneurysm sac from its parent vessel is required that yields the neck curve and the ostium. However, manual and subjective neck curve and ostium definitions might lead to inaccurate IA assessments.

Methods

The research project VICTORIA was initiated, allowing users to interactively define the neck curve of five segmented IA models using a web application. The submitted results were qualitatively and quantitatively compared to identify the minimum, median and maximum aneurysm surface area. Finally, image-based blood flow simulations were carried out to assess the effect of variable neck curve definitions on relevant flow- and shear-related parameters.

Results

In total, 55 participants (20 physicians) from 18 countries participated in VICTORIA. For relatively simple aneurysms, a good agreement with respect to the neck curve definition was found. However, differences among the participants increased with increasing complexity of the aneurysm. Furthermore, it was observed that the majority of participants excluded any small arteries occurring in the vicinity of an aneurysm. This can lead to non-negligible deviations among the flow- and shear-related parameters, which need to be carefully evaluated, if quantitative analysis is desired. Finally, no differences between participants with medical and non-medical background could be observed.

Conclusions

VICTORIAs findings reveal the complexity of aneurysm neck curve definition, especially for bifurcation aneurysms. Standardization appears to be mandatory for future sac-vessel-separations. For hemodynamic simulations a careful neck curve definition is crucial to avoid inaccuracies during the quantitative flow analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13239-021-00535-w.

How Cerebral Vessel Tortuosity Affects Development and Recurrence of Aneurysm: Outer Curvature versus Bifurcation Type

Background and Purpose

Previous studies have assessed the relationship between cerebral vessel tortuosity and intracranial aneurysm (IA) based on two-dimensional brain image analysis. We evaluated the relationship between cerebral vessel tortuosity and IA according to the hemodynamic location using three-dimensional (3D) analysis and studied the effect of tortuosity on the recurrence of treated IA.

Methods

We collected clinical and imaging data from patients with IA and disease-free controls. IAs were categorized into outer curvature and bifurcation types. Computerized analysis of the images provided information on the length of the arterial segment and tortuosity of the cerebral arteries in 3D space.

Results

Data from 95 patients with IA and 95 controls were analyzed. Regarding parent vessel tortuosity index (TI; P<0.01), average TI (P<0.01), basilar artery (BA; P=0.02), left posterior cerebral artery (P=0.03), both vertebral arteries (VAs; P<0.01), and right internal carotid artery (P<0.01), there was a significant difference only in the outer curvature type compared with the control group. The outer curvature type was analyzed, and the occurrence of an IA was associated with increased TI of the parent vessel, average, BA, right middle cerebral artery, and both VAs in the logistic regression analysis. However, in all aneurysm cases, recanalization of the treated aneurysm was inversely associated with increased TI of the parent vessels.

Conclusions

TIs of intracranial arteries are associated with the occurrence of IA, especially in the outer curvature type. IAs with a high TI in the parent vessel showed good outcomes with endovascular treatment.

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to cause a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of UIAs exist undiscovered until rupture. Current clinical practice in the detection of UIAs relies heavily on manual radiological review of standard imaging modalities. Recent computer-aided UIA diagnoses can sensitively detect and measure UIAs within cranial angiograms but remain limited to low specificities whose output also requires considerable radiologist interpretation not amenable to broad screening efforts. To address these limitations, we have developed a novel automatic pipeline algorithm which inputs medical images and outputs detected UIAs by characterising single-voxel morphometry of segmented neurovasculature. Once neurovascular anatomy of a specified resolution is segmented, correlations between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 min on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities.

Cerebral aneurysm rupture status classification using statistical and machine learning methods

Morphological characterization and fluid dynamics simulations were carried out to classify the rupture status of 71 (36 unruptured, 35 ruptured) patient specific cerebral aneurysms using a machine learning approach together with statistical techniques. Eleven morphological and six hemodynamic parameters were evaluated individually and collectively for significance as rupture status predictors. The performance of each parameter was inspected using hypothesis testing, accuracy, confusion matrix, and the area under the receiver operating characteristic curve. Overall, the size ratio exhibited the best performance, followed by the diastolic wall shear stress, and systolic wall shear stress. The prediction capability of all 17 parameters together was evaluated using eight different machine learning algorithms. The logistic regression achieved the highest accuracy (0.75), whereas the random forest had the highest area under curve value among all the classifiers (0.82), surpassing the performance exhibited by the size ratio. Hence, we propose the random forest model as a tool that can help improve the rupture status prediction of cerebral aneurysms.

Tobacco use and age are associated with different morphologic features of anterior communicating artery aneurysms

We present a cohort of patients with anterior communicating artery (ACoA) aneurysms to investigate morphological characteristics and clinical factors associated with rupture of the aneurysms. 505 patients with ACoA aneurysms were identified at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016, with available CT angiography (CTA). Three-dimensional (3D) reconstructions were performed to evaluate aneurysmal morphologic features, including location, projection, irregularity, the presence of daughter dome, height, height/width ratio, and relationships between surrounding vessels. Patient risk factors assessed included patient age, sex, tobacco use, alcohol use, and family history of aneurysms and aneurysmal subarachnoid hemorrhage. Logistic regression was used to build a predictive ACoA score for rupture. Morphologic features associated with ruptured ACoA aneurysms were the presence of a daughter dome (OR 21.4, 95% CI 10.6-43.1), smaller neck diameter (OR 0.55, 95% CI 0.42-0.71), larger aspect ratio (OR 3.57, 95% CI 2.05-6.24), larger flow angle (OR 1.03, 95% CI 1.02-1.05), and smaller ipsilateral A2-ACoA angle (OR 0.98, 95% CI 0.97-1.00). Tobacco use was predominantly associated with morphological factors intrinsic to the aneurysm that were associated with rupture while younger age was also associated with morphologic features extrinsic to the aneurysm that were associated with rupture. The ACoA score had good predictive capacity for rupture with AUC = 0.92 using the 0.632 bootstrap cross-validation for correction of overfitting bias. Ruptured ACoA aneurysms were associated with morphological features that are simple to assess using a simple scoring system. Tobacco use and younger age were predominantly associated with intrinsic and extrinsic morphological features characteristic of rupture, respectively.

Effect of combined acetylsalicylic acid and statins treatment on intracranial aneurysm rupture

Background

Acetylsalicylic acid (ASA) and statins have been identified as potentially reducing the risk of intracranial aneurysms (IA) rupture. We aim to determine the effect of this drugs on the risk of rupture of IA.

Patients and methods

We performed a retrospective cohort study from a prospective database of patients with IA treated in our institution between January 2013 and December 2018. Demographics, previous oral treatments, presence of multiple aneurysms, size of aneurysm, lobulation, location and morphology of the aneurysms were recorded. Patients were dichotomized as ruptured and unruptured IA.

Results

A total of 408 IA were treated, of which 283 (68.6%) were in women. The median age was 53, 194 (47.5%) were ruptured IA. 38 patients (9.3%) were receiving ASA and 84 (20.6%) were receiving statins at the moment of the IA diagnosis. In the multivariable regression analysis, ASA plus statin use and multiple aneurysms were independently associated with unruptured IA (OR 5.01, 95% CI, 1.37–18.33, P = 0.015 and OR 2.72, 95% CI 1.68–4.27, P<0.001, respectively). Whereas, lobulated wall aneurysm and PComA/AComA location were inversely and independently associated with unruptured IA condition (OR 0.34, 95% CI 0.21–0.55, P<0.001 and OR 0.37, 95% CI 0.23–0.60, P<0.001, respectively). However, ASA and statins in monotherapy were not independently associated with unruptured IA condition.

Conclusions

In our study population ASA plus statins treatment is independently associated with unruptured IA. Larger and prospective studies are required to explore this potential protective effect against IA rupture.

Packing Technique with or without Remodeling for Endovascular Coil Embolization of Renal Artery Aneurysms: Safety, Efficacy and Mid-Term Outcomes

The endovascular treatment of renal artery aneurysms (RAAs) has lower morbidity and shorter stay lengths compared to surgical repair. Here, we describe coil packing with or without remodeling and assess outcomes and complications. We retrospectively identified the 19 consecutive preventive endovascular RAA coil embolizations done in 18 patients at our center in 2010-2020. Patient and aneurysm characteristics, technical success rate, complications, and recurrences were recorded. Mean patient age was 63 ± 13 years. The RAA was >1.5 cm in 11 cases, and in four cases, the aneurysm-to-parent artery size ratio was >2. Simple coiling was performed for 11 (57.9%) aneurysms, stent-assisted coiling for seven (36.8%) aneurysms, and balloon-assisted coiling for one (5.3%) aneurysm. Technical success rate was 100%. Complete definitive RAA exclusion was achieved with a single procedure for 17 (89.5%) aneurysms, whereas two (10.5%) aneurysms required a repeat procedure. Four minor complications occurred but resolved with no long-term consequences. No major complications occurred during the mean follow-up of 41.1 ± 29.7 months. Coil embolization by sac packing or remodeling proved very safe and effective. Together with the known lower morbidity and shorter stay length compared to open surgery, these data indicate that this endovascular procedure should become the preventive treatment of choice for RAAs.

Risk Factors for Higher Volume of Hemorrhage in Ruptured Anterior Circulation Intracranial Aneurysms

Purpose: To explore the influencing factors of volume hemorrhage in ruptured anterior circulation aneurysms, so as to identify the characteristics of anterior circulation aneurysms with high volume of hemorrhage, and to provide advice for clinical diagnosis and treatment for those aneurysms. Methods: We retrospectively reviewed 437 cases of ruptured anterior intracranial aneurysms in our center between the years 2012 and 2017. According to the inclusion criteria, a total of 100 qualified patients were screened out. We collected demographic characteristics, environmental exposure, and admission status of enrolled patients. In addition, morphological parameters and location of aneurysms were also included. The semiautomatic threshold method was used to measure the volume of hemorrhage. According to the results, the patients were divided into the group with high blood volume and low blood volume. Univariate and multivariate logistic regression analyses were used to discover the related factors affecting the bleeding volume. Results: In univariable analysis, pulse pressure (P = 0.014) showed a significant difference at the P < 0.05 test level. In terms of aneurysm morphology, the irregular shape (P < 0.001), calcification (P = 0.001), and flow angle (P = 0.006) showed significant statistical differences between the two groups at the P < 0.01 level (P < 0.01). Multivariate logistic regression analysis showed that irregular shape (OR = 5.370 P = 0.002 < 0.05), large flow angle (OR = 1.033 P = 0.016 < 0.05), and calcification (OR = 5.460 P = 0.003 < 0.05) were risk factors for volume of hemorrhage in ruptured anterior circulation aneurysms. The influence of hypertension history was at critical state (OR = 2.877 P = 0.051 > 005). Conclusions: According to our analysis results, intracranial anterior circulation aneurysms with irregular shapes, calcifications, and large flow angle are more dangerous. Aneurysms with these characteristics often have a large amount of hemorrhage, requiring timely treatment in clinical practice.

Characteristics of patients with ruptured very small intracranial aneurysm sized less than 3 mm

Objective

If the size of an intracranial aneurysm is below 3 mm, clinicians rarely treat them because of the low risk of rupture. But subarachnoid hemorrhage (SAH) due to the rupture of very small intracranial aneurysm (VSIA) (saccular aneurysm sized less than 3 mm) may lead to many critical neurological complications. So we analyzed the characteristics and differences between the ruptured VSIA group and the ruptured non-VSIA group.

Methods

421 saccular aneurysms from patients with SAH between January 2016 and December 2019 were included. Patient information including age, sex, and medical history and information about the aneurysm including location, size, aspect ratio, inflow angle, and height-width ratio were collected. And we compared the VSIA group with non-VSIA group about these characteristics

Results

12.1% (51/421) of the aneurysms were included in the VSIA group, while the non-VSIA group consisted of 87.9% of the aneurysms (370/421). The female predominance was significantly higher in the VSIA group than that in the non-VSIA group (p=0.011). No significant difference was observed in location, medical history, height-width ratio between the groups. The mean value of the inflow angle in the VSIA group was much lower than that in the non-VSIA group, but no statistically significant association between rupture risk and the inflow angle was observed. The average aspect ratio was significantly lower than that in the non-VSIA group.

Conclusions

Ruptured VSIA group has higher percentage of females and lower aspect ratio than ruptured non-VSIA group. Further studies regarding the characteristics of ruptured and unruptured VSIA patients is required for assistance in clinical decision related to treatment of VSIA group before the aneurysmal sac rupture.

Residual Flow Inside the Woven EndoBridge Device at Follow-Up: Potential Predictors of the Bicêtre Occlusion Scale Score 1 Phenomenon

BACKGROUND AND PURPOSE

The Woven EndoBridge (WEB) device is an established technique for the treatment of intracranial aneurysms. Occasionally, persistent opacification inside the WEB lumen can be observed at follow-up (previously described as Bicêtre Occlusion Scale Score 1). We evaluated potential risk factors of this phenomenon, hypothesizing that initial deviation of the WEB device from the aneurysm axis, size of the aneurysmal neck surface, or inappropriate WEB sizing correlates with Bicêtre Occlusion Scale Score 1 findings.

MATERIALS AND METHODS

We systematically reviewed all patients treated with the WEB device between February 2014 and December 2018 in our neurointerventional center. Patients with midterm follow-up DSA available were considered for aneurysm evaluation applying the Bicêtre Occlusion Scale Score. WEB angle deviation from the aneurysm axis, neck widths, and WEB sizes were collected.

RESULTS

We included 65 patients with 67 intracranial aneurysms. Eleven of 67 (16.4%) intracranial aneurysms showed the Bicêtre Occlusion Scale Score 1 phenomenon at follow-up. Anterior-posterior projections of WEB axis deviation (angles measured in degrees) were significantly different between the Bicêtre Occlusion Scale Score 1 cohort (median ± interquartile range, 17 ± 17) and all other Bicêtre Occlusion Scale Scores (median ± interquartile range, 7 ± 11; P = .023), whereas in lateral projections, no significant difference was observed (median ± interquartile range, 10 ± 10 versus 8 ± 9; P = .169). Neck or aneurysm recurrence, but not the Bicêtre Occlusion Scale Score 1 phenomenon, occurred significantly more often in patients with inappropriate WEB sizing compared with appropriate WEB sizing (median ± interquartile range, 1 ± 1.3 versus 0 ± 0; P < .001/P = .664).

CONCLUSIONS

The Bicêtre Occlusion Scale Score 1 phenomenon is associated with an initial deviation of the WEB device from the aneurysm axis but does not correlate with aneurysmal neck surface measurements or WEB sizing.

Size of ruptured intracranial aneurysms: a systematic review and meta-analysis

BACKGROUND

There is wide variation in the reported size of ruptured intracranial aneurysms and methods of size estimation. There is widespread belief that small aneurysms < 7 mm do not rupture. Therefore, we performed a systematic review and meta-analysis of the literature to determine the size of ruptured aneurysms according to aneurysm locations and methods of size estimation.

METHODS

We searched PubMed, Cochrane, CINAHL, and EMBASE databases using a combination of Medical Subject Headings (MeSH) terms. We included articles that reported mean aneurysm size in consecutive series of ruptured intracranial. We excluded studies limited to a specific aneurysm location or type. The random-effects model was used to calculate overall mean size and location-specific mean size. We performed meta-regression to explain observed heterogeneity and variation in reported size.

RESULTS

The systematic review included 36 studies and 12,609 ruptured intracranial aneurysms. Overall mean aneurysm size was 7.0 mm (95% confidence interval [CI 6.2-7.4]). Pooled mean size varied with location. Overall mean size of 2145 ruptured anterior circulation aneurysms was 6.0 mm (95% CI 5.6-6.4, residual I² = 86%). Overall mean size of 743 ruptured posterior circulation aneurysms was 6.2 mm (95% CI 5.3-7.0, residual I² = 93%). Meta-regression identified aneurysm location and definition of size (i.e., maximum dimension vs. aneurysm height) as significant determinants of aneurysm size reported in the studies.

CONCLUSIONS

The mean size of ruptured aneurysms in most studies was approximately 7 mm. The general wisdom that aneurysms of this size do not rupture is incorrect. Location and size definition were significant determinants of aneurysm size.

Discrepancy between two-dimensional and three-dimensional digital subtraction angiography for the planning of endovascular coiling of small cerebral aneurysms <5 mm

BACKGROUND

To investigate the discrepancy between two-dimensional digital subtraction angiography and three-dimensional rotational angiography for small (<5 mm) cerebral aneurysms and the impact on decision making among neuro-interventional experts as evaluated by online questionnaire.

MATERIALS AND METHODS

Eight small (<5 mm) ruptured aneurysms were visually identified in 16 image sets in either two-dimensional or three-dimensional format for placement in a questionnaire for 11 invited neuro-interventionalists. For each set, two questions were posed: Question 1: "Which of the following is the preferred treatment choice: simple coiling, balloon remodeling or stent assisted coiling?"; Question 2: "Is it achievable to secure the aneurysm with pure simple coiling?" The discrepancies of angio-architecture parameters and treatment choices between two-dimensional-digital subtraction angiography and three-dimensional rotational angiography were evaluated.

RESULTS

In all eight cases, the neck images via three-dimensional rotational angiography were larger than two-dimensional-digital subtraction angiography with a mean difference of 0.95 mm. All eight cases analyzed with three-dimensional rotational angiography, but only one case with two-dimensional-digital subtraction angiography were classified as wide-neck aneurysms with dome-to-neck ratio < 1.5. The treatment choices based on the two-dimensional or three-dimensional information were different in 56 of 88 (63.6%) paired answers. Simple coiling was the preferred choice in 66 (75%) and 26 (29.6%) answers based on two-dimensional and three-dimensional information, respectively. Three types of angio-architecture with a narrow gap between the aneurysm sidewall and parent artery were proposed as an explanation for neck overestimation with three-dimensional rotational angiography.

CONCLUSIONS

Aneurysm neck overestimation with three-dimensional rotational angiography predisposed neuro-interventionalists to more complex treatment techniques. Additional two-dimensional information is crucial for endovascular treatment planning for small cerebral aneurysms.

Automatic Diagnosis Based on Spatial Information Fusion Feature for Intracranial Aneurysm

Timely and accurate auxiliary diagnosis of intracranial aneurysm can help radiologist make treatment plans quickly, saving lives and cutting costs at the same time. At present, Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of intracranial aneurysm, but as radiologists interpret those imaging sequences frame by frame, misdiagnosis might occur. The utilization of computer-aided diagnosis (CAD) can ease the burdens of radiologists and improve the detection accuracy of aneurysms. In this article, a deep learning method is applied to detect the intracranial aneurysm in 3D Rotational Angiography (3D-RA) based on a spatial information fusion (SIF) method, and instead of a 3D vascular model, 2D image sequences are used. Given the intracranial aneurysm and vascular overlap having similar feature in the most time, rather than focusing on distinguishing them in one frame, the morphological differences between frames are considered as major feature. In the training data, consecutive frames of every imaging time series are extracted and concatenated in a specific way, so that the spatial contextual information could be embedded into a single two-dimensional image. This method enables the time series with obvious correlation between frames be directly trained on 2D convolutional neural network (CNN), instead of 3D-CNN with huge computational cost. Finally, we got an accuracy of 98.89%, with sensitivity and specificity of 99.38% and 98.19%, respectively, which proves the feasibility and availability of the SIF feature.

Comparison of Aneurysm Patency and Mural Inflammation in an Arterial Rabbit Sidewall and Bifurcation Aneurysm Model under Consideration of Different Wall Conditions

Background: Biological processes that lead to aneurysm formation, growth and rupture are insufficiently understood. Vessel wall inflammation and degeneration are suggested to be the driving factors. In this study, we aimed to investigate the natural course of vital (non-decellularized) and decellularized aneurysms in a rabbit sidewall and bifurcation model. Methods: Arterial pouches were sutured end-to-side on the carotid artery of New Zealand White rabbits (vital [n = 6] or decellularized [n = 6]), and into an end-to-side common carotid artery bifurcation (vital [n = 6] and decellularized [n = 6]). Patency was confirmed by fluorescence angiography. After 28 days, all animals underwent magnetic resonance and fluorescence angiography followed by aneurysm harvesting for macroscopic and histological evaluation. Results: None of the aneurysms ruptured during follow-up. All sidewall aneurysms thrombosed with histological inferior thrombus organization observed in decellularized compared to vital aneurysms. In the bifurcation model, half of all decellularized aneurysms thrombosed whereas the non-decellularized aneurysms remained patent with relevant increase in size compared to baseline. Conclusions: Poor thrombus organization in decellularized sidewall aneurysms confirmed the important role of mural cells in aneurysm healing after thrombus formation. Several factors such as restriction by neck tissue, small dimensions and hemodynamics may have prevented aneurysm growth despite pronounced inflammation in decellularized aneurysms. In the bifurcation model, rarefication of mural cells did not increase the risk of aneurysm growth but tendency to spontaneous thrombosis.

Cellular and Molecular Probing of Intact Human Organs

Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challenging to render transparent because of the accumulation of dense and sturdy molecules in decades-aged tissues. To overcome these challenges, we developed SHANEL, a method based on a new tissue permeabilization approach to clear and label stiff human organs. We used SHANEL to render the intact adult human brain and kidney transparent and perform 3D histology with antibodies and dyes in centimeters-depth. Thereby, we revealed structural details of the intact human eye, human thyroid, human kidney, and transgenic pig pancreas at the cellular resolution. Furthermore, we developed a deep learning pipeline to analyze millions of cells in cleared human brain tissues within hours with standard lab computers. Overall, SHANEL is a robust and unbiased technology to chart the cellular and molecular architecture of large intact mammalian organs.

Effects of size and elasticity on the relation between flow velocity and wall shear stress in side-wall aneurysms: A lattice Boltzmann-based computer simulation study

Blood flow in an artery is a fluid-structure interaction problem. It is widely accepted that aneurysm formation, enlargement and failure are associated with wall shear stress (WSS) which is exerted by flowing blood on the aneurysmal wall. To date, the combined effect of aneurysm size and wall elasticity on intra-aneurysm (IA) flow characteristics, particularly in the case of side-wall aneurysms, is poorly understood. Here we propose a model of three-dimensional viscous flow in a compliant artery containing an aneurysm by employing the immersed boundary-lattice Boltzmann-finite element method. This model allows to adequately account for the elastic deformation of both the blood vessel and aneurysm walls. Using this model, we perform a detailed investigation of the flow through aneurysm under different conditions with a focus on the parameters which may influence the wall shear stress. Most importantly, it is shown in this work that the use of flow velocity as a proxy for wall shear stress is well justified only in those sections of the vessel which are close to the ideal cylindrical geometry. Within the aneurysm domain, however, the correlation between wall shear stress and flow velocity is largely lost due to the complexity of the geometry and the resulting flow pattern. Moreover, the correlations weaken further with the phase shift between flow velocity and transmural pressure. These findings have important implications for medical applications since wall shear stress is believed to play a crucial role in aneurysm rupture.

Bifurcation Configuration Is an Independent Risk Factor for Aneurysm Rupture Irrespective of Location

Background: Bifurcation and sidewall aneurysms have different rupture risks, but whether this difference comes from the location of the aneurysm is not clear. The objective of this study is to illustrate the rationality of ranking bifurcation configuration as an independent risk factor for aneurysm rupture.

Methods: Morphological features of 719 aneurysms (216 ruptured) were automatically extracted from a consecutive cohort of patients via PyRadiomics. Rupture risks and morphological features were compared between bifurcation and sidewall aneurysms, and lasso regression was applied to explore the morphological determinants for rupture in bifurcation and sidewall aneurysms. Rupture risks and morphological features of bifurcation aneurysms in different locations were analyzed. Multivariate regression was performed to explore the risk factors for aneurysm rupture.

Results: Twelve morphological features were automatically extracted from PyRadiomics implemented in Python. The rupture risks were higher in bifurcation aneurysms (P < 0.01), and morphological features Elongation and Flatness were much lower in ruptured bifurcation than sidewall aneurysms (P = 0.036, 0.011, respectively). Elongation and Flatness were the morphological determinants for rupture in bifurcation aneurysms, whereas Elongation and SphericalDisproportion were determinants for sidewall aneurysms. Different rupture risks and morphological features were found between sidewall and bifurcation aneurysms of the same location, and among bifurcation aneurysms of different locations. In multivariate regression, bifurcation configuration was an independent risk factor for aneurysm rupture (OR 3.007, 95% CI 1.752–5.248, P < 0.001).

Conclusions: Sidewall and bifurcation aneurysms and bifurcation aneurysms of different locations have different rupture risks and morphological features. Bifurcation configuration is an independent risk factor for aneurysm rupture irrespective of location.

Aneurysmal Subarachnoid Hemorrhage Associated with Small Aneurysms in Smokers and Women: A Retrospective Analysis

Background

Previous studies have shown low rupture rates for small aneurysms (<10 mm), suggesting that the risk of treatment could exceed the benefits. However, evidence has changed, showing crescent rates of aneurysmal subarachnoid hemorrhage (aSAH) associated with small aneurysms. We report trends in size, localization, clinical characteristics, and outcomes of intracranial aneurysms (IAs).

Methods

In this retrospective study, a total of 200 clinical histories of patients diagnosed with IAs over an 8-year period were analyzed. Variables considered included age, sex, tobacco consumption, morphological characteristics of the aneurysm, complications, vasospasm, and mortality. Qualitative variables were assessed by measurements of absolute and relative frequency. Smoking behavior, aneurysm size, and aneurysm rupture (AR) were compared using 1-way analysis of variance. Categorical variables were analyzed using Pearson's χ² test.

Results

The average age at presentation was 58 years. The average size of ruptured aneurysms in the general group was 2.5–7.5 mm, and AR was most common in women (76%) and in patients age 50–60 years (33%). The rate of vasospasm was 19%, and mortality was 37%. Smokers composed 32% of the cohort. Heavy smokers had a 57% rate of aSAH, with an average size of rupture of 5 mm. The most common location of aneurysms and AR was the AComA (33%).

Conclusions

Our results suggest increasing AR rates in aneurysms smaller than 10 mm. This trend is seen especially in individuals with heavy tobacco consumption and in women of perimenopausal age. Our findings show a tendency of AR in accordance with previous results and are expected to serve as basis for further research on aneurysm management.

Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features

Background and Purpose- Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in small aneurysms. This study aims to evaluate the feasibility of applying machine learning for predicting aneurysm stability with radiomics-derived morphological features. Methods- Morphological features of 719 aneurysms were extracted from PyRadiomics, of which 420 aneurysms with Maximum3DDiameter ranging from 4 mm to 8 mm were enrolled for analysis. The stability of these aneurysms and other clinical characteristics were reviewed from the medical records. Based on the morphologies with/without clinical features, machine learning models were constructed and compared to define the morphological determinants and screen the optimal model for predicting aneurysm stability. The effect of clinical characteristics on the morphology of unstable aneurysms was analyzed. Results- Twelve morphological features were automatically extracted from PyRadiomics implemented in Python for each aneurysm. Lasso regression defined Flatness as the most important morphological feature to predict aneurysm stability, followed by SphericalDisproportion, Maximum2DDiameterSlice, and SurfaceArea. SurfaceArea (odds ratio [OR], 0.697; 95% CI, 0.476-0.998), SphericalDisproportion (OR, 1.730; 95% CI, 1.143-2.658), Flatness (OR, 0.584; 95% CI, 0.374-0.894), Hyperlipemia (OR, 2.410; 95% CI, 1.029-5.721), Multiplicity (OR, 0.182; 95% CI, 0.082-0.380), Location at middle cerebral artery (OR, 0.359; 95% CI, 0.134-0.902), and internal carotid artery (OR, 0.087; 95% CI, 0.030-0.211) were enrolled into the final prediction model. In terms of performance, the area under curve of the model reached 0.853 (95% CI, 0.767-0.940). For unstable aneurysms, Compactness1 (P=0.035), Compactness2 (P=0.036), Sphericity (P=0.035), and Flatness (P=0.010) were low, whereas SphericalDisproportion (P=0.034) was higher in patients with hypertension. Conclusions- Morphological features extracted from PyRadiomics can be used for aneurysm stratification. Flatness is the most important morphological determinant to predict aneurysm stability. Our model can be used to predict aneurysm stability. Unstable aneurysm is more irregular in patients with hypertension.

Size, Aspect Ratio and Anatomic Location of Ruptured Intracranial Aneurysms: Consecutive Series of 415 Patients from a Prospective, Multicenter, Observational Study

To analyze the size and location distribution of ruptured intracranial aneurysms (IAs) helps to provide evidence for clinical treatment of unruptured IAs using this feature of aneurysms. In this study, 415 patients who presented with an acute subarachnoid hemorrhage caused by IAs were enrolled from eight tertiary referral centers between June 2016 and March 2018. The size, aspect ratio and anatomic location of ruptured IAs were defined and reported by patient sex. In the study cohort of 415 patients (60.5% women) with saccular ruptured IAs, the three most common locations of ruptured IAs were posterior communicating artery (32.0%), anterior communicating artery (28.7%), and middle cerebral artery (13.5%). The mean size of all ruptured IAs was 5.3±3.1 mm (range 1.1-28.5 mm), but the size varied considerably by location. For example, ruptured IAs of the posterior communicating artery had a mean size of 5.8±3.1 mm, whereas the mean size of ruptured anterior communicating artery aneurysms was 4.6±1.7 mm. The mean AR in all ruptured IAs was 1.66±0.76. Of those aneurysms, 243 (58.6%) had an AR smaller than 1.6 and 318 (76.6%) had an AR smaller than 2.0. Our results suggested that the size of the most ruptured IAs are smaller than 7 mm or even 5 mm. The size and AR varied by sex and location. With the knowledge of size, location and AR, multiplicity should be considered for treatment strategies of unruptured IAs.