Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): uncertainty quantification of geometric rupture risk parameters

BackgroundGeometric parameters have been proposed for prediction of cerebral aneurysm rupture risk. Predicting the rupture risk for incidentally detected unruptured aneurysms could help clinicians in their treatment decision. However, assessment of geometric parameters depends on several factors, including the spatial resolution of the imaging modality used and the chosen reconstruction procedure. The aim of this study was to investigate the uncertainty of a variety of previously proposed geometric parameters for rupture risk assessment, caused by variability of reconstruction procedures.Materials26 research groups provided segmentations and surface reconstructions of five cerebral aneurysms as part of the Multiple Aneurysms AnaTomy CHallenge (MATCH) 2018. 40 dimensional and non-dimensional geometric parameters, describing aneurysm size, neck size, and irregularity of aneurysm shape, were computed. The medians as well as the absolute and relative uncertainties of the parameters were calculated. Additionally, linear regression analysis was performed on the absolute uncertainties and the median parameter values.ResultsA large variability of relative uncertainties in the range between 3.9 and 179.8% was found. Linear regression analysis indicates that some parameters capture similar geometric aspects. The lowest uncertainties < 6% were found for the non-dimensional parameters isoperimetric ratio, convexity ratio, and ellipticity index. Uncertainty of 2D and 3D size parameters was significantly higher than uncertainty of 1D parameters. The most extreme uncertainties > 80% were found for some curvature parameters.ConclusionsUncertainty analysis is essential on the road to clinical translation and use of rupture risk prediction models. Uncertainty quantification of geometric rupture risk parameters provided by this study may help support development of future rupture risk prediction models.

[1]  Birgitta K Velthuis,et al.  PHASES Score for Prediction of Intracranial Aneurysm Growth , 2015, Stroke.

[2]  Alexander Keedy,et al.  An overview of intracranial aneurysms , 2006, McGill journal of medicine : MJM : an international forum for the advancement of medical sciences by students.

[3]  M. L. Raghavan,et al.  Quantified aneurysm shape and rupture risk. , 2005, Journal of neurosurgery.

[4]  Andrzej Materka,et al.  Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms , 2016, Comput. Methods Programs Biomed..

[5]  Sabine Schilling,et al.  PHASES Score for the Management of Intracranial Aneurysm: A Cross-Sectional Population-Based Retrospective Study , 2017, Stroke.

[6]  E. Ross Association , 1886, American Journal of Sociology.

[7]  C. Prestigiacomo,et al.  Predicting aneurysm rupture probabilities through the application of a computed tomography angiography-derived binary logistic regression model. , 2009, Journal of neurosurgery.

[8]  Fujimaro Ishida,et al.  Low Wall Shear Stress Is Independently Associated With the Rupture Status of Middle Cerebral Artery Aneurysms , 2013, Stroke.

[9]  J. Mocco,et al.  MORPHOLOGY PARAMETERS FOR INTRACRANIAL ANEURYSM RUPTURE RISK ASSESSMENT , 2008, Neurosurgery.

[10]  J. Sayre,et al.  Comparative Morphological Analysis of the Geometry of Ruptured and Unruptured Aneurysms , 2011, Neurosurgery.

[11]  Yuichi Murayama,et al.  Unruptured Intracranial Aneurysms: Incidence of Rupture and Risk Factors , 2009, Stroke.

[12]  Toshihiro Ishibashi,et al.  Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies , 2014, The Lancet Neurology.

[13]  S Saalfeld,et al.  Does the DSA reconstruction kernel affect hemodynamic predictions in intracranial aneurysms? An analysis of geometry and blood flow variations , 2017, Journal of NeuroInterventional Surgery.

[14]  S. Fukuhara,et al.  The natural course of unruptured cerebral aneurysms in a Japanese cohort. , 2012, The New England journal of medicine.

[15]  J Max Findlay,et al.  The aspect ratio (dome/neck) of ruptured and unruptured aneurysms. , 2003, Journal of neurosurgery.

[16]  B. Hoh,et al.  BOTTLENECK FACTOR AND HEIGHT‐WIDTH RATIO: ASSOCIATION WITH RUPTURED ANEURYSMS IN PATIENTS WITH MULTIPLE CEREBRAL ANEURYSMS , 2007, Neurosurgery.

[17]  J. Mocco,et al.  Hemodynamic–Morphologic Discriminants for Intracranial Aneurysm Rupture , 2011, Stroke.

[18]  E. Connolly,et al.  Guidelines for the Management of Patients With Unruptured Intracranial Aneurysms: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association , 2015, Stroke.

[19]  Philipp Berg,et al.  Multiple intracranial aneurysms: a direct hemodynamic comparison between ruptured and unruptured vessel malformations , 2017, International Journal of Computer Assisted Radiology and Surgery.

[20]  T. Hori,et al.  Is the Aspect Ratio a Reliable Index for Predicting the Rupture of a Saccular Aneurysm? , 2001, Neurosurgery.

[21]  C M Putman,et al.  Computational fluid dynamics modeling of intracranial aneurysms: effects of parent artery segmentation on intra-aneurysmal hemodynamics. , 2006, AJNR. American journal of neuroradiology.

[22]  Ashley F. Emery,et al.  Models and Uncertainty , 2014 .

[23]  A. Algra,et al.  PHASES and the natural history of unruptured aneurysms: science or pseudoscience? , 2016, Journal of NeuroInterventional Surgery.

[24]  Fernando Mut,et al.  Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics , 2018, International Journal of Computer Assisted Radiology and Surgery.

[25]  Olivier Brina,et al.  Towards the Clinical utility of CFD for assessment of intracranial aneurysm rupture – a systematic review and novel parameter-ranking tool , 2018, Journal of NeuroInterventional Surgery.

[26]  B. Bendok,et al.  Unruptured intracranial aneurysms and the assessment of rupture risk based on anatomical and morphological factors: sifting through the sands of data. , 2009, Neurosurgical focus.

[27]  Fernando Mut,et al.  Development of a statistical model for discrimination of rupture status in posterior communicating artery aneurysms , 2018, Acta Neurochirurgica.

[28]  三浦 洋一,et al.  Low wall shear stress is independently associated with the rupture status of middle cerebral artery aneurysms , 2013 .

[29]  Lucas O. Müller,et al.  Uncertainty Quantification and Sensitivity Analysis for Computational FFR Estimation in Stable Coronary Artery Disease , 2018, Cardiovascular Engineering and Technology.

[30]  Bernhard Preim,et al.  Semiautomatic neck curve reconstruction for intracranial aneurysm rupture risk assessment based on morphological parameters , 2018, International Journal of Computer Assisted Radiology and Surgery.

[31]  Show-Ling Jang,et al.  Size and location of ruptured intracranial aneurysms. , 2009, Journal of Korean Neurosurgical Society.

[32]  Jean Raymond,et al.  PHASES and the natural history of unruptured aneurysms: science or pseudoscience? , 2016, Journal of NeuroInterventional Surgery.

[33]  Eric L. Miller,et al.  3D Shape Analysis of Intracranial Aneurysms Using the Writhe Number as a Discriminant for Rupture , 2011, Annals of Biomedical Engineering.

[34]  Alejandro F. Frangi,et al.  Virtual endovascular treatment of intracranial aneurysms: models and uncertainty , 2017, Wiley interdisciplinary reviews. Systems biology and medicine.

[35]  Andreas Raabe,et al.  Fourier analysis of intracranial aneurysms: towards an objective and quantitative evaluation of the shape of aneurysms , 2005, Neuroradiology.

[36]  Thomas Wagner,et al.  Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation , 2018, Cardiovascular Engineering and Technology.

[37]  B Notarberardino,et al.  An efficient approach to converting three-dimensional image data into highly accurate computational models , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[38]  D. Nichols,et al.  Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment , 2003, The Lancet.

[39]  Bernhard Preim,et al.  3DRA Reconstruction of Intracranial Aneurysms – How does Voxel Size Influences Morphologic and Hemodynamic Parameters , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[40]  J. Schaller,et al.  Statistical wall shear stress maps of ruptured and unruptured middle cerebral artery aneurysms , 2012, Journal of The Royal Society Interface.

[41]  Je Hoon Oh,et al.  Influence of Parent Artery Segmentation and Boundary Conditions on Hemodynamic Characteristics of Intracranial Aneurysms , 2015, Yonsei medical journal.

[42]  Dae-Won Kim,et al.  Rupture of Very Small Intracranial Aneurysms: Incidence and Clinical Characteristics , 2015, Journal of cerebrovascular and endovascular neurosurgery.

[43]  M. L. Raghavan,et al.  Three-Dimensional Geometrical Characterization of Cerebral Aneurysms , 2004, Annals of Biomedical Engineering.

[44]  Alberto Avolio,et al.  Image segmentation methods for intracranial aneurysm haemodynamic research. , 2014, Journal of biomechanics.

[45]  Eric L. Miller,et al.  Rupture Status Discrimination in Intracranial Aneurysms Using the Centroid–Radii Model , 2011, IEEE Transactions on Biomedical Engineering.

[46]  L Goubergrits,et al.  Reproducibility of Image-Based Analysis of Cerebral Aneurysm Geometry and Hemodynamics: An In-Vitro Study of Magnetic Resonance Imaging, Computed Tomography, and Three-Dimensional Rotational Angiography , 2013, Journal of Neurological Surgery—Part A.