Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24987
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dc.contributor.authorJuchler, Norman-
dc.contributor.authorSchilling, Sabine-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorKurtcuoglu, Vartan-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2022-05-18T11:44:25Z-
dc.date.available2022-05-18T11:44:25Z-
dc.date.issued2022-05-03-
dc.identifier.issn1664-2295de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24987-
dc.description.abstractBackground: 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.de_CH
dc.language.isoende_CH
dc.publisherFrontiers Research Foundationde_CH
dc.relation.ispartofFrontiers in Neurologyde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectIntracranial aneurysmde_CH
dc.subjectQuantitative morphologyde_CH
dc.subjectImage-based analysisde_CH
dc.subjectRupture status predictionde_CH
dc.subjectShape irregularityde_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleShape trumps size : image-based morphological analysis reveals that the 3D shape discriminates intracranial aneurysm disease status better than aneurysm sizede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.3389/fneur.2022.809391de_CH
dc.identifier.doi10.21256/zhaw-24987-
zhaw.funding.euNode_CH
zhaw.issue809391de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume13de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf183774de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.funding.zhawAneuXde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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