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


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
08 11 2022
Historique:
entrez: 9 11 2022
pubmed: 10 11 2022
medline: 15 11 2022
Statut: epublish

Résumé

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. 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. 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. 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.

Identifiants

pubmed: 36351732
pii: bmjopen-2022-063051
doi: 10.1136/bmjopen-2022-063051
pmc: PMC9644336
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e063051

Informations de copyright

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Florian Hellmeier (F)

Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Jan Brüning (J)

Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany jan.bruening@charite.de.

Philipp Berg (P)

Laboratory of Fluid Dynamics and Technical Flows, University of Magdeburg, Magdeburg, Germany.
Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany.

Sylvia Saalfeld (S)

Research Campus STIMULATE, University of Magdeburg, Magdeburg, Germany.
Department of Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.

Andreas Spuler (A)

Helios Klinikum Berlin-Buch, Berlin, Germany.

Ibrahim Erol Sandalcioglu (IE)

Department of Neurosurgery, University Hospital Magdeburg, Magdeburg, Germany.

Oliver Beuing (O)

Department of Radiology, AMEOS Hospital Bernburg, Bernburg, Germany.

Naomi Larsen (N)

Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein (UKSH), Kiel, Germany.

Jens Schaller (J)

Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Leonid Goubergrits (L)

Institute of Computer-Assisted Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Einstein Center Digital Future, Berlin, Germany.

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