Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis.

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making.

Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors.

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

Shape irregularity of the intracranial aneurysm lumen exhibits diagnostic value.

BACKGROUND: Morphological irregularity is linked to intracranial aneurysm wall instability and manifests in the lumen shape. Yet there is currently no consent on how to assess shape irregularity. The aims of this work are to quantify irregularity as perceived by clinicians, to break down irregularity into morphological attributes, and to relate these to clinically relevant factors such as rupture status, aneurysm location, and patient age or sex. METHODS: Thirteen clinicians and 26 laypersons assessed 134 aneurysm lumen segmentations in terms of overall perceived irregularity and five different morphological attributes (presence/absence of a rough surface, blebs, lobules, asymmetry, complex geometry of the parent vasculature). We examined rater agreement and compared the ratings with clinical factors by means of regression analysis or binary classification. RESULTS: Using rank-based aggregation, the irregularity ratings of clinicians and laypersons did not differ statistically. Perceived irregularity showed good agreement with curvature (coefficient of determination R2 = 0.68 ± 0.08) and was modeled very accurately using the five morphological rating attributes plus shape elongation (R2 = 0.95 ± 0.02). In agreement with previous studies, irregularity was associated with aneurysm rupture status (AUC = 0.81 ± 0.08); adding aneurysm location as an explanatory variable increased the AUC to 0.87 ± 0.09. Besides irregularity, perceived asymmetry, presence of blebs or lobules, aneurysm size, non-sphericity, and curvature were linked to rupture. No association was found between morphology and any of patient sex, age, and history of smoking or hypertension. Aneurysm size was linked to morphology. CONCLUSIONS: Irregular lumen shape carries significant information on the aneurysm's disease status. Irregularity constitutes a continuous parameter that shows a strong association with the rupture status. To improve the objectivity of morphological assessment, we suggest examining shape through six different morphological attributes, which can characterize irregularity accurately.

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.

PURPOSE: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status. METHODS: Hemodynamic, morphological, and patient-related information of 1631 cerebral aneurysms characterized by computational fluid dynamics simulations were used to train support vector machines (SVMs) with linear and RBF kernel (RBF-SVM), k-nearest neighbors (kNN), decision tree, random forest, and multilayer perceptron (MLP) neural network classifiers for predicting the aneurysm rupture status. The classifiers' accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated and compared to the LRM using 249 test cases obtained from two external cohorts. Additionally, important variables were determined based on the random forest and weights of the linear SVM. RESULTS: The AUCs of the MLP, LRM, linear SVM, RBF-SVM, kNN, decision tree, and random forest were 0.83, 0.82, 0.80, 0.81, 0.76, 0.70, and 0.79, respectively. The accuracy ranged between 0.76 (decision tree,) and 0.79 (linear SVM, RBF-SVM, and MLP). Important variables for predicting the aneurysm rupture status included aneurysm location, the mean surface curvature, and maximum flow velocity. CONCLUSION: The performance of the LRM was overall comparable to that of the other ML classifiers, confirming its potential for aneurysm rupture assessment. To further improve the predictions, additional information, e.g., related to the aneurysm wall, might be needed.

Image-based mechanical analysis of stent deformation: concept and exemplary implementation for aortic valve stents.

An approach for extracting the radial force load on an implanted stent from medical images is proposed. To exemplify the approach, a system is presented which computes a radial force estimation from computer tomography images acquired from patients who underwent transcatheter aortic valve implantation (TAVI). The deformed shape of the implanted valve prosthesis' Nitinol frame is extracted from the images. A set of displacement vectors is computed that parameterizes the observed deformation. An iterative relaxation algorithm is employed to adapt the information extracted from the images to a finite-element model of the stent, and the radial components of the interaction forces between the stent and the tissue are extracted. For the evaluation of the method, tests were run using the clinical data from 21 patients. Stent modeling and extraction of the radial forces were successful in 18 cases. Synthetic test cases were generated, in addition, for assessing the sensitivity to the measurement errors. In a sensitivity analysis, the geometric error of the stent reconstruction was below 0.3 mm, which is below the image resolution. The distribution of the radial forces was qualitatively and quantitatively reasonable. An uncertainty remains in the quantitative evaluation of the radial forces due to the uncertainty in defining a radial direction on the deformed stent. With our approach, the mechanical situation of TAVI stents after the implantation can be studied in vivo, which may help to understand the mechanisms that lead to the complications and improve stent design.

Simulation of transcatheter aortic valve implantation under consideration of leaflet calcification.

Transcatheter aortic valve implantation (TAVI) is a minimally invasive off-pump procedure to replace diseased aortic heart valves. Known complications include paravalvular leaks, atrioventricular blocks, coronary obstruction and annular rupture. Careful procedure planning including appropriate stent selection and sizing are crucial. Few patient-specific geometric parameters, like annular diameters, annular perimeter and measurement of the distance to the coronary ostia, are currently used within this process. Biomechanical simulation allows the consideration of extracted anatomy and material parameters for the intervention, which may improve planning and execution phases. We present a simulation workflow using a fully segmented aortic root anatomy, which was extracted from pre-operative CT-scan data and apply individual material models and parameters to predict the procedure outcome. Our results indicate the high relevance of calcification location and size for intervention planning, which are not sufficiently considered at this time. Our analysis can further provide guidance for accurate, patient-specific device positioning and future adaptations to stent design.

Physiologically based construction of optimized 3-D arterial tree models.

We present an approach to generate 3-D arterial tree models based on physiological principles while at the same time certain morphological properties are enforced at construction time in order to build individual vascular models down to the capillary level. The driving force of our approach is an angiogenesis model incorporating case-specific information about the metabolic activity in the considered domain. Additionally, we enforce morphometrically confirmed bifurcation statistics of vascular networks. The proposed method is able to generate artificial, yet physiologically plausible, arterial tree models that match the metabolic demand of the embedding tissue and fulfill the enforced morphological properties at the same time. We demonstrate the plausibility of our method on synthetic data for different metabolic configurations and analyze physiological and morphological properties of the generated tree models.