BMJ Open (Nov 2022)

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

  • Oliver Beuing,
  • Philipp Berg,
  • Leonid Goubergrits,
  • Andreas Spuler,
  • Florian Hellmeier,
  • Jan Brüning,
  • Sylvia Saalfeld,
  • Ibrahim Erol Sandalcioglu,
  • Naomi Larsen,
  • Jens Schaller

DOI
https://doi.org/10.1136/bmjopen-2022-063051
Journal volume & issue
Vol. 12, no. 11

Abstract

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Objectives Assessing the risk associated with unruptured intracranial aneurysms (IAs) is essential in clinical decision making. Several geometric risk parameters have been proposed for this purpose. However, performance of these parameters has been inconsistent. This study evaluates the performance and robustness of geometric risk parameters on two datasets and compare it to the uncertainty inherent in assessing these parameters and quantifies interparameter correlations.Methods Two datasets containing 244 ruptured and unruptured IA geometries from 178 patients were retrospectively analysed. IAs were stratified by anatomical region, based on the PHASES score locations. 37 geometric risk parameters representing four groups (size, neck, non-dimensional, and curvature parameters) were assessed. Analysis included standardised absolute group differences (SADs) between ruptured and unruptured IAs, ratios of SAD to median relative uncertainty (MRU) associated with the parameters, and interparameter correlation.Results The ratio of SAD to MRU was lower for higher dimensional size parameters (ie, areas and volumes) than for one-dimensional size parameters. Non-dimensional size parameters performed comparatively well with regard to SAD and MRU. SAD was higher in the posterior anatomical region. Correlation of parameters was strongest within parameter (sub)groups and between size and curvature parameters, while anatomical region did not strongly affect correlation patterns.Conclusion Non-dimensional parameters and few parameters from other groups were comparatively robust, suggesting that they might generalise better to other datasets. The data on discriminative performance and interparameter correlations presented in this study may aid in developing and choosing robust geometric parameters for use in rupture risk models.