Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

Detmer, Felicitas J.; Chung, Bong Jae; Mut, Fernando; Slawski, Martin; Hamzei-Sichani, Farid; Putman, Christopher M.; Jimenez, Carlos; Cebral, Juan R.

doi:10.1007/s11548-018-1837-0

Cited by 54 publications

(81 citation statements)

References 49 publications

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“…The rupture probability model has been previously developed using cross-sectional image and patient data from 1,061 patients with 1,631 aneurysms obtained from hospitals in the US, Japan, and Colombia [10]. For the external evaluation of this model, cross-sectional data of two patient cohorts, the AneuRisk dataset 1 [27] and datasets from the AneuX project, which had not been used for model training, were used.…”

Section: Methodsmentioning

confidence: 99%

“…The images of the training data had been segmented using in-house software with a thresholding-based approach [10]. The AneuRisk data were segmented using the “Vascular Modeling ToolKit” (VMTK) with a gradientdriven level-set approach [23].…”

Section: Methodsmentioning

confidence: 99%

“…Using the segmented 3D aneurysm geometries, we performed CFD simulations as previously described for the training data [10]. Briefly, unstructured grids were automatically generated using tetrahedral elements with a maximum element size of 0.2 mm.…”

Section: Methodsmentioning

confidence: 99%

“…Next, the hemodynamic and morphological parameters of the aneurysm rupture probability model were automatically computed based on the CFD solutions as well as the 3D geometries as previously described [10]. …”

Section: Methodsmentioning

confidence: 99%

“…We recently developed a probability model for aneurysm rupture combining morphological, hemodynamic, and patient-related parameters [10]. The internal validation of the model with 1,631 IAs that were used for model training indicated a good predictive performance.…”

Section: Introductionmentioning

confidence: 99%

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External validation of cerebral aneurysm rupture probability model with data from two patient cohorts

et al. 2018

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Background: For a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions. Methods: Segmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model’s performance was further compared to a similaritybased approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts. Results: When applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC=0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC=0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model’s prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14). Conclusions: The model’s performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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External validation of cerebral aneurysm rupture probability model with data from two patient cohorts

et al. 2018

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Disturbed flow's impact on cellular changes indicative of vascular aneurysm initiation, expansion, and rupture: A pathological and methodological review

Sunderland

Jiang

Zhao

2021

Journal Cellular Physiology

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Aneurysms are malformations within the arterial vasculature brought on by the structural breakdown of the microarchitecture of the vessel wall, with aneurysms posing serious health risks in the event of their rupture. Blood flow within vessels is generally laminar with high, unidirectional wall shear stressors that modulate vascular endothelial cell functionality and regulate vascular smooth muscle cells. However, altered vascular geometry induced by bifurcations, significant curvature, stenosis, or clinical interventions can alter the flow, generating low stressor disturbed flow patterns. Disturbed flow is associated with altered cellular morphology, upregulated expression of proteins modulating inflammation, decreased regulation of vascular permeability, degraded extracellular matrix, and heightened cellular apoptosis. The understanding of the effects disturbed flow has on the cellular cascades which initiate aneurysms and promote their subsequent growth can further elucidate the nature of this complex pathology. This review summarizes the current knowledge about the disturbed flow and its relation to aneurysm pathology, the methods used to investigate these relations, as well as how such knowledge has impacted clinical treatment methodologies. This information can contribute to the understanding of the development, growth, and rupture of aneurysms and help develop novel research and aneurysmal treatment techniques.

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Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Stumpo

Staartjes

Esposito

et al. 2021

Acta Neurochirurgica Supplement

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Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

Abstract: The model combining variables from various domains was able to discriminate between ruptured and unruptured aneurysms with an AUC of 86%. Internal validation indicated potential for the application of this model in clinical practice after evaluation with longitudinal data.

Cited by 54 publications

References 49 publications

External validation of cerebral aneurysm rupture probability model with data from two patient cohorts

External validation of cerebral aneurysm rupture probability model with data from two patient cohorts

Disturbed flow's impact on cellular changes indicative of vascular aneurysm initiation, expansion, and rupture: A pathological and methodological review

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

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