Rupture Risk Of Intracranial Aneurysms Research Articles

CD6 and CCR7 as Genetic Biomarkers in Evaluating Intracranial Aneurysm Rupture Risk.

This study used bioinformatics combined with statistical methods to identify plasma biomarkers that can predict intracranial aneurysm (IA) rupture and provide a strong theoretical basis for the search for new IA rupture prevention methods. We downloaded gene expression profiles in the GSE36791 and GSE122897 datasets from the Gene Expression Omnibus (GEO) database. Data were normalized using the "sva" R package and differentially expressed genes (DEGs) were identified using the "limma" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used for DEG function analysis. Univariate logistic regression analysis, least absolute shrinkage and selection operator (LASSO) regression modeling, and the support vector machine recursive feature elimination (SVM-RFE) algorithm were used to identify key biomarker genes. Data from GSE122897 and GSE13353 were extracted to verify our findings. Eight co-DEG mRNAs were identified in the GSE36791 and GSE122897 datasets. Genes associated with inflammatory responses were clustered in the co-DEG mRNAs in IAs. CD6 and C-C chemokine receptor 7 (CCR7) were identified as key genes associated with IA. CD6 and CCR7 were upregulated in patients with IA and their expression levels were positively correlated. There were significant differences in the infiltration of immune cells between IAs and normal vascular wall tissues (p < 0.05). A predictive nomogram was designed using this two-gene signature. Binary transformation of CD6 and CCR7 was performed according to the cut-off value to construct the receiver-operating characteristic (ROC) curve and showed a strong predictive ability of the CD6-CCR7 gene signature (p < 0.01; area under the curve (AUC): 0.90; 95% confidence interval (CI): 0.88-0.92). Furthermore, validation of this two-gene signature using the GSE122897 and GSE13353 datasets proved it to be valuable for clinical application. The identified two-gene signature (CD6-CCR7) for evaluating the risk of IA rupture demonstrated good clinical application value.

Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model.

Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.

Unveiling rupture risk and clinical outcomes in midline aneurysms: A matched cohort analysis investigating the impact of localization within the anterior or posterior circulation.

Intracranial aneurysms (IAs) located in the anterior and posterior circulations of the Circle of Willis present differential rupture risks. This study aimed to compare the rupture risk and clinical outcomes of anterior communicating artery aneurysms (AcomA) and basilar tip aneurysms (BAs); two IA types located along the midline within the Circle of Willis. We retrospectively collected data from 1026 patients presenting with saccular IAs. Only AcomA and BAs with a 3D angiography were included. Out of 186 included IAs, a cohort of 32 BAs was matched with AcomA based on the patients' pre-existing conditions and morphological parameters of IAs. Clinical outcomes, including rupture risk, hydrocephalus development, vasospasm incidence, and patients' outcome, were compared. The analysis revealed no significant difference in rupture risk, development of hydrocephalus, need for ventricular drainage, or vasospasm incidence between the matched AcomA and BA cohorts. Furthermore, the clinical outcomes post-rupture did not significantly differ between the two groups, except for a higher Fisher Grade associated with BAs. Once accounting for morphological and patient factors, the rupture risk between AcomA and BAs is comparable. These findings underscore the importance of tailored management strategies for specific IA types and suggest that further investigations should focus on the role of individual patient and aneurysm characteristics in IA rupture risk and clinical outcomes.

Abstract TP3: Predicting Rupture Risk of Intracranial Aneurysms Through Radiomics Analysis

Background: Intracranial aneurysm rupture is a devastating event. Accurate estimation of their rupture risk can help guide management decisions. However, existing prediction methods remain subjective and have limited accuracy. This study is aimed at using clinical and imaging data to create an accurate predictive clinical tool for identification of aneurysms at a higher risk of rupturing. Methods: A prospectively acquired database of patients scanned with 3T high-resolution MRI between 2018 to 2023 was analyzed. Symptomatic status was defined as aneurysms that caused a cranial nerve neuropathy due to compression, and/or presented with a sentinel headache. Saccular aneurysms were segmented within 3D Slicer using co-registered T1 and T1+Gd 3T images. Morphological and aneurysm wall enhancement (AWE) metrics were obtained from the 3D models. Radiomic features (RFs) were extracted using pyRadiomics extension of 3D Slicer. This database was utilized for training and testing different machine learning prediction models. Finally, a Naïve bayes model was then employed for constructing clinical nomograms (Figure). Results: A total of one hundred and four aneurysms were analyzed. Twenty-nine were symptomatic. There were 87 RFs significantly different between symptomatic and asymptomatic aneurysms. Four machine learning prediction models were created based on clinical, morphological, AWE and RFs data. The best predictive model was based on RFs, achieving an accuracy of 74%. Conclusion: RFs combined with clinical, morphological and AWE metrics could potentially be a valuable clinical tool to predict intracranial aneurysms rupture risk.

Possible association between β-blocker use and a risk of intracranial aneurysm rupture.

Aneurysmal subarachnoid hemorrhage is a devastating type of stroke, associated with high mortality and morbidity. One of modifiable risk factors of aneurysm rupture is hypertension, however, it is still not clear whether any particular antihypertensive drugs play a significant role in the prevention of aneurysm rupture. We decided to investigate whether there is any association between acetylsalicylic acid, α-blockers, β‑blockers, angiotensin‑converting enzyme inhibitors, angiotensin II receptor blockers, calcium channel blockers, diuretics, statins, and anticoagulants and a risk of intracranial aneurysm rupture. We retrospectively analyzed 334 patients with ruptured and unruptured intracranial aneurysm. Based on logistic regression models, we obtained unadjusted and adjusted odds ratios (ORs) of subarachnoid hemorrhage associated with the use of vasoactive medications and with indices of tortuosity. We found that β‑blocker intake was significantly related to higher tortuosity of the cerebral arteries. Also, the intake of β‑blockers (OR, 0.41; 95% CI, 0.21-0.77; P = 0.01) and statins (OR, 0.23; 95% CI, 0.05-0.68; P = 0.01) significantly decreased the risk of aneurysm rupture, a result driven by a decreased rupture risk of anterior circulation aneurysms. No such association was found for the posterior part of the cerebral circulation. Aneurysm located in the anterior cerebral circulation might be less likely to rupture if patients receive β‑blockers or statins.

Intracranial aneurysm rupture risk in northern China: a retrospective case-control study.

Rupture of intracranial aneurysms (IAs) can cause subarachnoid hemorrhage (SAH), which leads to severe neurological dysfunction and even death. Exploring the risk factors for IA rupture and taking preventive measures accordingly can reduce or prevent the occurrence of SAH. Currently, there is still no consensus on the detrimental factors for IA rupture. Thus, our study aimed to investigate the risk factors of IA rupture in a population of northern China. We systematically collected the demographic features, medical history, and imaging data of aneurysms from patients with ruptured and unruptured IAs (UIAs) who attended the Department of Neurosurgery at the Second Hospital of Hebei Medical University from 2014 to 2019. All cases had been diagnosed by digital subtraction angiography. We excluded patients with SAH resulting from injuries, as well as those with vascular dissection and incomplete data. Finally, 1,214 patients including 616 with ruptured IAs and 598 with UIAs were collected for further analysis. A case-control study was conducted, in which multivariable logistic regression was used to analyze the risk factors for IA rupture. Our multivariable logistic regression showed that anterior cerebral artery [odds ratio (OR) =2.413; 95% confidence interval (CI): 1.235-4.718], anterior communicating artery (OR =3.952; 95% CI: 2.601-6.006), posterior communicating artery (OR =2.385; 95% CI: 1.790-3.177), and anterior circulation branches (OR =3.493; 95% CI: 1.422-8.581) were risk factors for IA rupture, whereas patients with a history of cerebral infarction (OR =0.395; 95% CI: 0.247-0.631) and those with IAs located in the internal carotid artery (OR =0.403; 95% CI: 0.292-0.557) were less likely to have IA rupture. IAs at specific locations are prone to rupture. These IAs should be paid particular attention and preventive measures should be taken to reduce or prevent their rupture.

Assessing the risk of intracranial aneurysm rupture using computational fluid dynamics: a pilot study.

This study compared 2 representative cases with ruptured aneurysms to explore the role of hemodynamic and morphological parameters in evaluating the rupture risk of intracranial aneurysms (IAs). CTA and 3-dimensional rotational angiography (3DRA) of 3 IAs in 2 patients were retrospectively analyzed in this study. Hemodynamics and morphological parameters were compared between a ruptured IA and an unruptured IA in case1, and between before and after aneurysm rupture in case 2. In case 1, the ruptured aneurysm had larger morphological parameters including size ratio (SR), aspect ratio (AR), aneurysm vessel angle (θF), Aneurysm inclination angle (θA), Undulation index (UI), Ellipticity index (EI), and Non-sphericity Index (NSI) than the unruptured aneurysm. And oscillatory shear index (OSI) is also larger. Higher rupture resemblance score (RRS) was shown in the ruptured aneurysm. In case 2, the aneurysm had one daughter sac after 2 years. Partial morphological and hemodynamic parameters including SR, AR, θF, θA, UI, EI, NSI, OSI, and relative residence time (RRT) increased, and normalized wall shear stress (NWSS) was significantly reduced. RRS increased during this period. SR and OSI may have predictive values for the risk of intracranial aneurysm rupture. It is possible that WSS Changes before and after IA rupture, yet the influence of high or low WSS on growth and rupture of IA remains unclear. RRS is promising to be used in the clinical assessment of the rupture risk of IAs and to guide the formulation of treatment plans.

Usage of computational method for hemodynamic analysis of intracranial aneurysm rupture risk in different geometrical aspects

The importance of the parent vessel geometrical feature on the risk of cerebral aneurysm rupture is unavoidable. This study presents inclusive details on the hemodynamics of Internal carotid artery (ICA) aneurysms with different parent vessel mean diameters. Different aspects of blood hemodynamics are compared to find a reasonable connection between parent vessel mean diameter and significant hemodynamic factors of wall shear stress (WSS), oscillatory shear index (OSI), and pressure distribution. To access hemodynamic data, computational fluid dynamics is used to model the blood stream inside the cerebral aneurysms. A hemodynamic comparison of the selected cerebral aneurysm shows that the minimum WSS is reduced by about 71% as the parent vessel’s mean diameter is increased from 3.18 to 4.48 mm.

Location specific rupture risk of intracranial aneurysms: case of ophthalmic aneurysms

BackgroundAneurysm location is a key element in predicting the rupture risk of an intracranial aneurysm. A common impression suggests that pure ophthalmic aneurysms are under-represented in ruptured intracranial aneurysms (RIAs)....

A novel clinical-radscore nomogram for predicting ruptured intracranial aneurysm

ObjectivesOur study aims to find the more practical and powerful method to predict intracranial aneurysm (IA) rupture through verification of predictive power of different models. MethodsClinical and imaging data of 576 patients with IAs including 192 ruptured IAs and matched 384 unruptured IAs was retrospectively analyzed. Radiomics features derived from computed tomography angiography (CTA) images were selected by t-test and Elastic-Net regression. A radiomics score (radscore) was developed based on the optimal radiomics features. Inflammatory markers were selected by multivariate regression. And then 4 models including the radscore, inflammatory, clinical and clinical-radscore models (C–R model) were built. The receiver operating characteristic curve (ROC) was performed to evaluate the performance of each model, PHASES and ELAPSS. The nomogram visualizing the C–R model was constructed to predict the risk of IA rupture. ResultsFive inflammatory features, 2 radiological characteristics and 7 radiomics features were significantly associated with IA rupture. The areas under ROCs of the radscore, inflammatory, clinical and C–R models were 0.814, 0.935, 0.970 and 0.975 in the training cohort and 0.805, 0.927, 0.952 and 0.962 in the validation cohort, respectively. ConclusionThe inflammatory model performs particularly well in predicting the risk of IA rupture, and its predictive power is further improved by combining with radiological and radiomics features and the C–R model performs the best. The C–R nomogram is a more stable and effective tool than PHASES and ELAPSS for individually predicting the risk of rupture for patients with IA.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm.

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges.

Intracranial aneurysms (IAs) are highly prevalent in the population, and their rupture poses a significant risk of death or disability. However, the treatment of aneurysms, whether through interventional embolization or craniotomy clipping surgery, is not always safe and carries a certain proportion of morbidity and mortality. Therefore, early detection and prompt intervention of IAs with a high risk of rupture is of notable clinical significance. Moreover, accurately predicting aneurysms that are likely to remain stable can help avoid the risks and costs of over-intervention, which also has considerable social significance. Recent advances in artificial intelligence (AI) technology offer promising strategies to assist clinical trials. This review will discuss the state-of-the-art AI applications for assessing the rupture risk of IAs, with a focus on achievements, challenges, and potential opportunities.

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

BackgroundThe presence of blebs increases the rupture risk of intracranial aneurysms (IAs).ObjectiveTo evaluate whether cross-sectional bleb formation models can identify aneurysms with focalized enlargement in longitudinal series.MethodsHemodynamic, geometric, and anatomical...

Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms

Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors, and to improve the life quality and expectancy of patients. This study aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework that combines state-of-the-art numerical methods to accurately describe the mechanical exchanges between the blood flow, the aneurysm, and the flow-deviator and deep reinforcement learning algorithms to identify a new stent concepts enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state.

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional statistical methods in predicting aneurysm rupture risk. This retrospective study included 1740 patients with 1809 intracranial aneurysms confirmed by digital subtraction angiography at two hospitals in China from January 2014 to December 2018. We randomly divided the dataset (hospital 1) into training (80%) and internal validation (20%). External validation was performed using independent data collected from hospital 2. The prediction models were developed based on clinical, aneurysm morphological, and radiomics parameters by logistic regression (LR). Additionally, the DL model for predicting aneurysm rupture risk using integration parameters was developed and compared with other models. The AUCs of LR models A (clinical), B (morphological), and C (radiomics) were 0.678, 0.708, and 0.738, respectively (all p < 0.05). The AUCs of the combined feature models D (clinical and morphological), E (clinical and radiomics), and F (clinical, morphological, and radiomics) were 0.771, 0.839, and 0.849, respectively. The DL model (AUC = 0.929) outperformed the machine learning (ML) (AUC = 0.878) and the LR models (AUC = 0.849). Also, the DL model has shown good performance in the external validation datasets (AUC: 0.876 vs 0.842 vs 0.823, respectively). Radiomics signatures play an important role in predicting aneurysm rupture risk. DL methods outperformed conventional statistical methods in prediction models for the rupture risk of unruptured intracranial aneurysms, integrating clinical, aneurysm morphological, and radiomics parameters. • Radiomics parameters are associated with the rupture risk of intracranial aneurysms. • The prediction model based on integrating parameters in the deep learning model was significantly better than a conventional model. • The radiomics signature proposed in this study could guide clinicians in selecting appropriate patients for preventive treatment.

163 Integrative Genomics Analysis Implicates Decreased FGD6 Expression Underlying Risk of Intracranial Aneurysm Rupture

INTRODUCTION: The genetic determinants of intracranial aneurysm rupture (rIA) are largely unknown. To identify and characterize genetic risk factors for rIA, we perform a genome-wide association study (GWAS) containing 84,353 individuals (7,843 rIA cases and 76,510 controls). We then use functional genomics approaches to delineate the mechanistic consequences of rIA risk loci. METHODS: We perform a meta-analysis across 24 published GWAS of rIA. Logistic regression was used to identify rIA-associated single nucleotide polymorphisms (SNPs). Gene-burden, expression quantitative trait loci (eQTL) mapping, and analysis of publicly-available single-cell RNA sequencing (scRNA-seq) data from normal human brain endothelial and perivascular cells was used to delineate cell-lineage expression of rIA-associated genes. RESULTS: We identify 5 independent genetic loci reaching genome-wide significance (p &lt; 5.0 x 10-8) for rIA: rs73392700 (CDKN2B, OR = 1.12), rs6841581 (EDNRA, OR = 0.77), rs11661542 (RBBP8, OR=0.84), rs62516550 (RP1, OR = 1.20), and rs12310399 (FGD6, OR = 1.16). FGD6, to our knowledge, has not been implicated in prior GWAS of (r)IA. We then quantified gene-level mutation-burden across ∼20,000 genes, and only FGD6 (containing 21 rIA-associated SNPs) reached transcriptome-wide significance. Functional eQTL mapping indicates that rs12310399 causes decreased FGD6 gene expression in arterial tissue. scRNA-seq identified high expression of FGD6 in 1 of 3 arterial lineages, but absent in perivascular cells, identifying the likely cell-lineage underlying FGD6-mediated rIA risk. CONCLUSIONS: We perform the largest genetic study of rIA to date. We identify and characterize a previously unknown risk loci for rIA containing FGD6. Elucidation of high-risk genetic loci may instruct population-genetic screening and clinical-genetic testing strategies to identify patients predisposed to rIA enabling early treatment and improved patient outcomes.

A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is . Existing methods relying on morphological features (e.g., height-width ratio) measured manually by neuroradiologists are labor intensive and have limited use for risk assessment. Therefore, we propose an end-to-end deep-learning method, called TransIAR net, to automatically learn the morphological features from 3D computed tomography angiography (CTA) data and accurately predict the status of IA rupture. We devise a multiscale 3D convolutional neural network (CNN) to extract the structural patterns of the IA and its neighborhood with a dual branch of shared network structures. Moreover, we learn the spatial dependence within the IA neighborhood with a transformer encoder. Our experiments demonstrated that the features learned by TransIAR are more effective and robust than handcrafted features, resulting in a improvement in the accuracy of rupture status prediction.

Mean arterial pressure-aneurysm neck ratio predicts the rupture risk of intracranial aneurysm by reflecting pressure at the dome.

The unruptured intracranial aneurysm (UIA) has high disability and mortality rate after rupture, it is particularly important to assess the risk of UIA and to carry out individualized treatment. The objective of this research is to introduce a novel parameter to predict the rupture risk of UIA. A total of 649 patients with 964 intracranial aneurysms in our center were enrolled. A novel parameter named mean arterial pressure-aneurysmal neck ratio (MAPN) was defined. Ten baseline clinical features and twelve aneurysm morphological characteristics were extracted to generate the MAPN model. The discriminatory performance of the MAPN model was compared with the PHASES score and the UCAS score. In hemodynamic analysis, MAPN was positively correlated with wall shear stress and aneurysm top pressure, with Pearson correlation coefficients of 0.887 and 0.791, respectively. The MAPN was larger in the ruptured group (36.62 ± 18.96 vs. 28.38 ± 14.58, P < 0.001). The area under the curve (AUC) of the MAPN was superior than the AUC of aspect ratio (AR) and the bottleneck factor (BN), they were 0.64 (P < 0.001; 95% CI, 0.588-0.692), 0.611 (P < 0.001; 95% CI, 0.559-0.663) and 0.607 (P < 0.001; 95% CI, 0.554-0.660), respectively. The MAPN model constructed by aneurysm size, aneurysm location, presence of secondary sacs and MAPN, demonstrated good discriminatory ability. The MAPN model exhibited superior performance compared with the UCAS score and the PHASES score (the AUC values were 0.799 [P < 0.001; 95% CI, 0.756-0.840], 0.763 [P < 0.001; 95% CI,0.719-0.807] and 0.741 [P < 0.001; 95% CI, 0.695-0.787], respectively; the sensitivities were 0.849, 0.758 and 0.753, respectively). Research demonstrates the potential of MAPN to augment the clinical decision-making process for assessing the rupture risk of UIAs.

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

PurposeIntracranial 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.MethodsWe 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.ResultsThe 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%.ConclusionThe 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.

Profiling of Circulating Gene Expression Reveals Molecular Signatures Associated with Intracranial Aneurysm Rupture Risk.

Following detection, rupture risk assessment for intracranial aneurysms (IAs) is critical. Towards molecular prognostics, we hypothesized that circulating blood RNA expression profiles are associated with IA risk. We performed RNA sequencing on 68 blood samples from IA patients. Here, patients were categorized as either high or low risk by assessment of aneurysm size (≥ 5 mm = high risk) and Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site (PHASES) score (≥ 1 = high risk). Modified F-statistics and Benjamini-Hochberg false discovery rate correction was performed on transcripts per million-normalized gene counts. Protein-coding genes expressed in ≥ 50% of samples with a q value < 0.05 and an absolute fold-change ≥ 2 were considered significantly differentially expressed. Bioinformatics in Ingenuity Pathway Analysis was performed to understand the biology of risk-associated expression profiles. Association was assessed between gene expression and risk via Pearson correlation analysis. Linear discriminant analysis models using significant genes were created and validated for classification of high-risk cases. We analyzed transcriptomes of 68 IA patients. In these cases, 31 IAs were large (≥ 5 mm), while 26 IAs had a high PHASES score. Based on size, 36 genes associated with high-risk IAs, and two were correlated with the size measurement. Alternatively, based on PHASES score, 76 genes associated with high-risk cases, and nine of them showed significant correlation to the score. Similar ontological terms were associated with both gene profiles, which reflected inflammatory signaling and vascular remodeling. Prediction models based on size and PHASES stratification were able to correctly predict IA risk status, with > 80% testing accuracy for both. Here, we identified genes associated with IA risk, as quantified by common clinical metrics. Preliminary classification models demonstrated feasibility of assessing IA risk using whole blood expression.