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

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s12938-019-0657-y) contains supplementary material, which is available to authorized users.

3D‐printed, patient‐specific intracranial aneurysm models: From clinical data to flow experiments with endovascular devices

“…Additionally, the size of the aneurysm neck was found to be overestimated rather than underestimated. Such overestimation can lead to different flow patterns and misinterpretation of the final results 25,33–35 . As a result of the MATCH 2018, the size of the aneurysm can be cross‐validated to reduce potential segmentation errors, for example, by comparing the resulting 3D model with high‐resolution 2D digital subtraction angiography data.…”

Geometric uncertainty in intracranial aneurysm rupture status discrimination: a two-site retrospective study

“…Previous studies found high variability in the uncertainty of different geometric risk parameters as well as differences in their ability to differentiate between ruptured and unruptured IAs. [14][15][16] Furthermore, for geometric risk parameters with multiple alternative definitions, the specific implementation can affect the discriminative performance of the parameter itself and any derived parameters, as demonstrated by Lauric et al for parameters incorporating IA and neck size. 17 The current study was performed to facilitate optimal selection of geometric rupture risk parameters by identifying geometric parameters, which are good discriminators while also exhibiting low uncertainty.…”

CADA: Clinical Background and Motivation

“…Altogether more as 40 different geometric risk parameters were proposed [18]. Furthermore, hemodynamic parameters describing aneurysmal flow patterns as well as forces acting on aneurysmal wall such as wall shear stress or oscillatory shear index were proposed to predict aneurysm rupture.…”