The role of shape for aneurysm risk assessment

Although the shape of intracranial aneurysms and the geometry of the surrounding vasculature are commonly taken into account by clinicians when assessing and treating aneurysms, it remains dif- ficult to quantify shape and develop clinical guidelines or tools that accommodate aneurysm shape. Here, we present new evidence that aneurysm shape is a meaningful proxy for disease status, the re- sults of a benchmark analysis comparing novel and established measurement methods for their ability to discriminate between ruptured and unruptured aneurysms, and how these findings can be trans- lated into clinics. We conclude with a plea for multi-centric data collections and present our own contributions to it

Modeling the location-dependency of aneurysm shape : a morphometric comparative study

The typical characteristics of intracranial aneurysms vary for different anatomic locations. Here, we study the location-dependent variability of aneurysm shape, and propose means to model and visualize this variability. We further elaborate to which extent the configuration of the cerebral vasculature could affect the outcome of an aneurysmal lesion

Shape trumps size : image-based morphological analysis reveals that the 3D shape discriminates intracranial aneurysm disease status better than aneurysm size

Background: To date, it remains difficult for clinicians to reliably assess the disease status of intracranial aneurysms. As an aneurysm's 3D shape is strongly dependent on the underlying formation processes, it is believed that the presence of certain shape features mirrors the disease status of the aneurysm wall. Currently, clinicians associate irregular shape with wall instability. However, no consensus exists about which shape features reliably predict instability. In this study, we present a benchmark to identify shape features providing the highest predictive power for aneurysm rupture status. Methods: 3D models of aneurysms were extracted from medical imaging data (3D rotational angiographies) using a standardized protocol. For these aneurysm models, we calculated a set of metrics characterizing the 3D shape: Geometry indices (such as undulation, ellipticity and non-sphericity); writhe- and curvature-based metrics; as well as indices based on Zernike moments. Using statistical learning methods, we investigated the association between shape features and aneurysm disease status. This processing was applied to a clinical dataset of 750 aneurysms (261 ruptured, 474 unruptured) registered in the AneuX morphology database. We report here statistical performance metrics [including the area under curve (AUC)] for morphometric models to discriminate between ruptured and unruptured aneurysms. Results: The non-sphericity index NSI (AUC = 0.80), normalized Zernike energies ZsurfN (AUC = 0.80) and the modified writhe-index WL1mean (AUC = 0.78) exhibited the strongest association with rupture status. The combination of predictors further improved the predictive performance (without location: AUC = 0.82, with location AUC = 0.87). The anatomical location was a good predictor for rupture status on its own (AUC = 0.78). Different protocols to isolate the aneurysm dome did not affect the prediction performance. We identified problems regarding generalizability if trained models are applied to datasets with different selection biases. Conclusions: Morphology provided a clear indication of the aneurysm disease status, with parameters measuring shape (especially irregularity) being better predictors than size. Quantitative measurement of shape, alone or in conjunction with information about aneurysm location, has the potential to improve the clinical assessment of intracranial aneurysms

Exploring intracranial aneurysm instability markers to improve disease modeling

Intracranial aneurysm (IA) shape is proposed to be a predicting factor of rupture. In this study, using 3D-angiographies, surgical and histological images, we ranked 11 IAs according to different characteristics (homogeneity, aspect and thickness), and correlations between the different ranking systems were investigated. We showed positive correlations between IA morphology (normalized total Gaussian curvature, GLN) and wall aspect ranking, and between GLN and histology ranking. Correlations between increased GLN, inhomogeneity of IA wall aspect and thickness were shown. This exploratory study supports the GLN in its ability to quantify IA shape and to be used as an IA wall feature predictor

Radiological feature heterogeneity supports etiological diversity among patient groups in Meniere's disease

We aimed to determine the prevalence of radiological temporal bone features that in previous studies showed only a weak or an inconsistent association with the clinical diagnosis of Meniere's disease (MD), in two groups of MD patients (n = 71) with previously established distinct endolymphatic sac pathologies; i.e. the group MD-dg (ES degeneration) and the group MD-hp (ES hypoplasia). Delayed gadolinium-enhanced MRI and high-resolution CT data were used to determine and compare between and within (affected vs. non-affected side) groups geometric temporal bone features (lengths, widths, contours), air cell tract volume, height of the jugular bulb, sigmoid sinus width, and MRI signal intensity alterations of the ES. Temporal bone features with significant intergroup differences were the retrolabyrinthine bone thickness (1.04 ± 0.69 mm, MD-hp; 3.1 ± 1.9 mm, MD-dg; p < 0.0001); posterior contour tortuosity (mean arch-to-chord ratio 1.019 ± 0.013, MD-hp; 1.096 ± 0.038, MD-dg; p < 0.0001); and the pneumatized volume (1.37 [0.86] cm3, MD-hp; 5.25 [3.45] cm3, MD-dg; p = 0.03). Features with differences between the affected and non-affected sides within the MD-dg group were the sigmoid sinus width (6.5 ± 1.7 mm, affected; 7.6 ± 2.1 mm, non-affected; p = 0.04) and the MRI signal intensity of the endolymphatic sac (median signal intensity, affected vs. unaffected side, 0.59 [IQR 0.31-0.89]). Radiological temporal bone features known to be only weakly or inconsistently associated with the clinical diagnosis MD, are highly prevalent in either of two MD patient groups. These results support the existence of diverse-developmental and degenerative-disease etiologies manifesting with distinct radiological temporal bone abnormalities

Shape-Based Analysis of Intracranial Aneurysms

Intracranial aneurysms (IAs) are malformations of larger arteries in the brain that are associated with a structural weakening of the vessel wall. Unruptured IAs are prevalent in 2-5% of the population and are detected ever more frequently due to the increased availability of medical imaging. Albeit the majority of IAs develops asymptomatically, the rare rupture of an IA causing a subarachnoid hemorrhage can have detrimental effects on the patient’s health or even cause the patient’s death. Therefore, clinicians are more often faced with difficult treatment decisions where they must weigh the costs of treatment against the risks of aneurysms to rupture. So far it is not possible to non-invasively determine the condition of the affected vessel wall region. Clinicians are therefore seeking for biomarkers that describe the structural stability of IAs. IA morphology, as seen in angiographic imaging, holds the potential for such a biomarker. Recent pathobiological studies suggest that structural wall instability is reflected in the geometry of the aneurysm lumen. This thesis project investigated the imaging-based morphological assessment of IAs. A first, data-driven approach, was based on a quantitative shape analysis derived on 3D surface geometries of 750 aneurysms. The author benchmarked established and novel morphometric parameters in terms of their predictive capacity for the disease status of the aneurysm, with the non-sphericity index and normalized Zernike energies performing best. He observed that shape is a stronger predictor for disease status than aneurysm size alone and confirmed the existing belief that IA morphology is associated with rupture. A second, psychometric approach, addressed the indistinct notion of morphological irregularity used by clinicians to characterize IA shape. Based on rating data from 13 clinical experts and 26 laypersons, the perceived irregularity of 134 aneurysms was quantified and used to identify the morphological constituents of overall irregularity. The author demonstrated that irregularity represents a continuous characteristic, with the risk of rupture increasing as the irregularity increases. Both approaches revealed a pronounced dependency of the shape on the anatomical location of the aneurysm. Combining shape and location substantially improved the accuracy of classification models for the IA rupture status. Other clinical aspects such as patient sex, age, smoking status or a history of blood hypertension did not play a significant role in the experiments. For future work, it is of great importance that the scientific community establishes a reference database to which new datasets can be related. In terms of morphology, the AneuX morphology database, which was developed in the context of this thesis project, could serve as such a reference. This thesis provides a refined, standardized taxonomy for morphological characteristics and offers a methodology to quantify subjective assessments of shape by humans. It contributes a software toolbox for morphometric analyses, and a new multicentric database comprising 750 aneurysms. Based on the comprehensive study of quantitative shape features, the author promotes the use of non-sphericity and an objective notion of irregularity for the clinical assessment IA shape

Real and assumed insights : statistical models and imaging biomarkers for disease characterization of intracranial aneurysms

Clinical data science is an emerging discipline, owing to recent developments in the acquisition, storage and processing of large amounts of clinical data. The increasing wealth of data, however, demands for interdisciplinary collaborations, which imposes new challenges. The need for properly dealing with selection biases and establishing balanced databases becomes a key issue to be addressed in the field of digital health. Pre-existing beliefs about the disease sometimes are badly supported by evidence. Wrong assumptions or selection biases, however, will skew the subsequent analyses and mislead the interpretation of results. Subjective or approximate assessments by clinicians, just like missing/censored information about the patients, the data acquisition process or the pathology under examination make data scientific approaches introduce uncertainty about the data to be processed. All this requires robust approaches and very good domain knowledge to avoid false predictions. To successfully reach the clinically relevant statements calls for transparent methods and efficient tools for creating and communicating insights to practitioners. This talk will present some of the experiences made throughout our research on intracranial aneurysms and discuss how we manage these challenges. It will touch the detection of visual biomarkers with machine learning, present an approach to quantifying rather vaguely defined subjective estimations and demonstrate how visualization helps to detect clinical pathways in high dimensional diagnostic data

AneuX morphology database

The AneuX morphology database is an open-access, multi-centric database containing 3D geometries of 750 intracranial aneurysms curated in the context of the AneuX project (2015-2020). The database combines data from three different projects (AneuX, @neurIST and Aneurisk) standardized using a single processing pipeline

What's ugly? : shape-based analysis of intracranial aneurysms

It is exceedingly challenging to assess the clinical significance of intracranial aneurysms. Currently, clinicians associate aneurysm shape irregularity with wall instability. However, there is no consensus on which shape features reliably predict aneurysm rupture risk. Here we present a machine learning approach to tackle this problem: We implemented a classification pipeline to identify shape features with predictive power of aneurysm instability. 3D models of aneurysms are extracted from medical imaging data (mostly 3D rotational angiography) using a standardized vessel segmentation protocol. A variety of established representations of the 3D shape are calculated for the extracted aneurysm segment. These include the calculation of Zernike moment invariants (ZMI) and simpler geometry indices such as undulation, ellipticity and non-sphericity. Feature reduction techniques (for ZMI) and classification methods are applied to find patterns linking shape features to aneurysm stability in an exploratory way. This processing pipeline was applied to a clinical dataset of approximately 250 aneurysms registered in the AneurysmDataBase (SwissNeuroFoundation) and AneuriskWeb database. Classification based on ZMI alone allowed us to distinguish between sidewall and bifurcation aneurysms, but failed to forecast an aneurysm’s rupture status reliably. Remarkably, simpler geometry indices performed similarly well in rupture status prediction. It remains to be investigated whether further stratification of the aneurysms in terms of location, size and clinical factors will increase the robustness of the applied classification methods. This study was performed within the scope of the AneuX project, funded by SystemsX.ch, and received support by SNSF NCCR Kidney.CH.

Big Data : machine learning to identify shape biomarkers in intracranial aneurysm

An intracranial aneurysm is a disease of the cerebral blood vessel wall resulting in the deformation and enlargement of the vascular lumen. If the process of deformation remains active, the vessel wall may either rupture and produce a hemorrhage, or thrombosis and ischemia may occur. Although statistically safe, some IAs do rupture. The physician needs to decide what to do for the individual patient. Obviously, an aneurysm should only be treated if the treatment is less risky than the aneurysm itself. So far, no accepted criteria exist for individual assessment of aneurysm stability and there are no clear treatment guidelines. Consequently, there is currently no validated tool to help predict development or treatment outcomes for an individual aneurysm and physicians rely solely on their personal judgment. The AneuX consortium collects a comprehensive number of patient data sets to estimate the disease status of intracranial aneurysms. The starting point is the hypothesis that vessel 3D-shape can be used as an image biomarker. Research and development in the field require massive information integration realized by a diverse community of scientists, physicians and engineers involved in better understanding the biological processes, and the development of new tools to manage and treat patients. The available cases are collected in a unified database containing imaging and clinical patient information on intracranial aneurysms. The AneurysmDataBase hosted by the Swiss Neuro Foundation aspires to establish the standard for collecting and characterizing information about intracranial aneurysms. We develop web-based applications to inspect, analyze and display data for various users: clinicians, patients and industry. As an aneurysm’s 3D-shape is strongly linked to the underlying formation processes, it is believed that the presence or absence of certain shape features mirror the disease status of the aneurysm wall. The shape of the aneurysm and its circumjacent arterial lumen already plays a significant role in the qualitative assessment of the aneurysm. Currently, clinicians associate irregularity with wall instability. However, no consensus yet exists about which shape features reliably predict instability or whether there exist any that qualify as biomarkers of the disease status at all. Here we present a classification pipeline that allows us to identify shape features with the highest predictive power of aneurysm instability. 3D models of aneurysms are extracted from medical imaging data (mostly 3D rotational angiography) using a standardized vessel segmentation protocol. Subsequently, the aneurysm and adjacent segments of parent vessels are cut from the lumen replica of the vascular tree. A variety of established representations of the 3D shape are calculated for the extracted aneurysm segment. These include the calculation of Zernike moments (ZM), their invariants (ZMI) and simple geometry indices such as undulation, ellipticity or non-sphericity. Different feature reduction techniques (for ZMI) and machine-learning classification methods are applied to find linking patterns between shape features and aneurysm disease status. We will present a machine learning framework to identify imaging biomarkers for intracranial aneurysm. These biomarkers are used for assessing the stability of an aneurysm and finally weighed against an interventional risk to propose the best treatment strategy for a patient. The AneurysmDataBase is pivotal to realize studies on larger cohorts and we are presenting the current state and vision of this disease management platform. The database provides statistical knowledge about a lesion site and can e.g. serve design of new devices, by providing geometries for sizing of a device or for conducting Computational Fluid Dynamics