A data-driven model to identify high-risk aneurysms and guide management decisions: the Rupture Resemblance Score

Rajabzadeh-Oghaz, Hamidreza; Waqas, Muhammad; Veeturi, Sricharan S.; Vakharia, Kunal; Tso, Michael K; Snyder, Kenneth V.; Davies, Jason M; Siddiqui, Adnan H.; Levy, Elad I.; Meng, Hui

doi:10.3171/2020.5.jns193264

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…A logistic regression model can be developed based on the differences of morphologic and hemodynamic parameters including SR, WSS, and OSI between ruptured and unruptured IAs ( 46 ). The model has also been used to predict the rupture probability of unruptured aneurysms, thus measuring their similarity to ruptured aneurysms.…”

Section: Discussionmentioning

confidence: 99%

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

Zhu,

Zou,

Sun

et al. 2023

Front. Neurol.

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ObjectiveThis 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).MethodsCTA 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.ResultsIn 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.ConclusionSR 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.

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Section: Discussionmentioning

confidence: 99%

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

Zhu,

Zou,

Sun

et al. 2023

Front. Neurol.

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“…Upon detection of an unruptured IA, the next step is to determine whether surgical intervention is indicated. While no clear consensus hitherto exists, there are scoring systems such as the PHASES score, Ruptured Resemblance Score (RRS), and ELAPSS score which can guide clinical judgment (Greving et al, 2014;Thompson et al, 2015;Backes et al, 2017;Rajabzadeh-Oghaz et al, 2020). The PHASES score provides risks of IA rupture based on the following: population ethnicity, hypertension, age, size of aneurysm, earlier subarachnoid hemorrhage (SAH) from another IA, and site of IA.…”

Section: The Role Of Ai In Determining Aneurysm Rupture Risk and Prog...mentioning

confidence: 99%

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Gilotra,

Swarna,

Mani

et al. 2023

Front. Hum. Neurosci.

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IntroductionCerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.MethodsWe review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.ResultsThe incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.ConclusionThe implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

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“…These metrics were used, along with aneurysm SR, to calculate RRS for each case. High-risk cases were defined using size and RRS as: IAs with size greater than 5 mm [21], and IAs with an RRS greater than 30% [10,12] in their respective analysis.…”

Section: Image-based Computational Fluid Dynamicsmentioning

confidence: 99%

“…Hemodynamics is a key factor that drives IA pathophysiology, playing a critical role in aneurysm initiation, growth and rupture [9]. Studies using three-dimensional imaging and computational fluid dynamics (CFD) have identified and demonstrated the clinical utility of hemodynamic metrics associated with ruptured IAs [10,11]. For example, one such metric developed was rupture resemblance score (RRS), a multivariate logistic regression model based on size ratio (SR), normalized wall shear stress (WSS) and oscillatory shear index (OSI) that could accurately identify ruptured aneurysms with a sensitivity of 90% [12].…”

Section: Introductionmentioning

confidence: 99%

Aneurysm risk metrics and hemodynamics are associated with greater vessel wall enhancement in intracranial aneurysms

et al. 2021

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Vessel wall enhancement (VWE) in contrast-enhanced magnetic resonance imaging (MRI) is a potential biomarker for intracranial aneurysm (IA) risk stratification. In this study, we investigated the relationship between VWE features, risk metrics, morphology and hemodynamics in 41 unruptured aneurysms. We reconstructed the IA geometries from MR angiography and mapped pituitary stalk-normalized MRI intensity on the aneurysm surface using an in-house tool. For each case, we calculated the maximum intensity (CR stalk ) and IA risk (via size and the rupture resemblance score (RRS)). We performed correlation analysis to assess relationships between CR stalk and IA risk metrics (size and RRS), as well as each parameter encompassed in RRS, i.e. aneurysmal size ratio (SR), normalized wall shear stress (WSS) and oscillatory shear index. We found that CR stalk had a strong correlation (Pearson correlation coefficient, PCC = 0.630) with size and a moderate correlation (PCC = 0.472) with RRS, indicating an association between VWE and IA risk. Furthermore, CR stalk had a weak negative correlation with normalized WSS (PCC = −0.320) and a weak positive correlation with SR (PCC = 0.390). Local voxel-based analysis showed only a weak negative correlation between normalized WSS and contrast-enhanced MRI signal intensity (PCC = −0.240), suggesting that if low-normalized WSS induces enhancement-associated pathobiology, the effect is not localized.

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A data-driven model to identify high-risk aneurysms and guide management decisions: the Rupture Resemblance Score

Cited by 9 publications

References 27 publications

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

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

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Aneurysm risk metrics and hemodynamics are associated with greater vessel wall enhancement in intracranial aneurysms

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