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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Shape trumps size : image-based morphological analysis reveals that the 3D shape discriminates intracranial aneurysm disease status better than aneurysm size
Authors: Juchler, Norman
Schilling, Sabine
Bijlenga, Philippe
Kurtcuoglu, Vartan
Hirsch, Sven
et. al: No
DOI: 10.3389/fneur.2022.809391
Published in: Frontiers in Neurology
Volume(Issue): 13
Issue: 809391
Issue Date: 3-May-2022
Publisher / Ed. Institution: Frontiers Research Foundation
ISSN: 1664-2295
Language: English
Subjects: Intracranial aneurysm; Quantitative morphology; Image-based analysis; Rupture status prediction; Shape irregularity
Subject (DDC): 616: Internal medicine and diseases
Abstract: Background: To date, it remains difficult for clinicians to reliably assess the disease status of intracranial aneurysms. As an aneurysm's 3D shape is strongly dependent on the underlying formation processes, it is believed that the presence of certain shape features mirrors the disease status of the aneurysm wall. Currently, clinicians associate irregular shape with wall instability. However, no consensus exists about which shape features reliably predict instability. In this study, we present a benchmark to identify shape features providing the highest predictive power for aneurysm rupture status. Methods: 3D models of aneurysms were extracted from medical imaging data (3D rotational angiographies) using a standardized protocol. For these aneurysm models, we calculated a set of metrics characterizing the 3D shape: Geometry indices (such as undulation, ellipticity and non-sphericity); writhe- and curvature-based metrics; as well as indices based on Zernike moments. Using statistical learning methods, we investigated the association between shape features and aneurysm disease status. This processing was applied to a clinical dataset of 750 aneurysms (261 ruptured, 474 unruptured) registered in the AneuX morphology database. We report here statistical performance metrics [including the area under curve (AUC)] for morphometric models to discriminate between ruptured and unruptured aneurysms. Results: The non-sphericity index NSI (AUC = 0.80), normalized Zernike energies ZsurfN (AUC = 0.80) and the modified writhe-index WL1mean (AUC = 0.78) exhibited the strongest association with rupture status. The combination of predictors further improved the predictive performance (without location: AUC = 0.82, with location AUC = 0.87). The anatomical location was a good predictor for rupture status on its own (AUC = 0.78). Different protocols to isolate the aneurysm dome did not affect the prediction performance. We identified problems regarding generalizability if trained models are applied to datasets with different selection biases. Conclusions: Morphology provided a clear indication of the aneurysm disease status, with parameters measuring shape (especially irregularity) being better predictors than size. Quantitative measurement of shape, alone or in conjunction with information about aneurysm location, has the potential to improve the clinical assessment of intracranial aneurysms.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: AneuX
Appears in collections:Publikationen Life Sciences und Facility Management

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