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dc.contributor.authorJuchler, Norman-
dc.contributor.authorSchilling, Sabine-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorRüfenacht, Daniel-
dc.contributor.authorKurtcuoglu, Vartan-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2019-11-13T10:35:51Z-
dc.date.available2019-11-13T10:35:51Z-
dc.date.issued2019-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/18667-
dc.description.abstractIntracranial aneurysms are focal deformations of larger cerebral arteries that occur in 2-5% of the population. Although they remain quiescent most of the time, aneurysms may rupture at a rate of about 1% per year, leading to subarachnoid hemorrhage with potentially catastrophic effects on the patient. It is exceedingly challenging to predict the clinical fate of intracranial aneurysms. Currently, physicians associate shape irregularity with vessel wall instability. However, there is no consensus on which shape features reliably predict aneurysm rupture. Here we present two approaches that aim to eliminate the subjectivity of rater assessment. In a first approach, we have implemented a semi-automated classification pipeline to predict the rupture status using morphometric parameters. These parameters were computed from 3D geometries of intracranial aneurysms obtained from 3D rotational angiographies. The main objective of this first study was to identify morphometric parameters that efficiently encode the disease status and understand how well morphology predicts disease status in general. In the second study, we followed a psychometric approach to better understand how human raters assess aneurysm morphology. We acquired rating data from 39 clinical experts and informed laypersons on perceived irregularity and the presence of 5 different morphological attributes (presence of a rough surface, of blebs, lobules, asymmetry and a complex parent vasculature). We related this data to clinically relevant parameters using regression analysis and binary classification. Our investigations confirmed that aneurysm morphology provides significant information about the disease. For example, a logistic regression model based on perceived irregularity alone is able to discriminate relatively well ruptured from unruptured aneurysms (AUC=0.81±0.04). Extending that model by aneurysm location increased the AUC significantly to 0.87±0.08, suggesting that morphology varies with location. Morphometric parameters well correlating to perceived irregularity (e.g. non-sphericity NSI, or total Gaussian curvature GLN) likewise predict the disease status well, but to a lesser extent. More specific parameters are required that are able to encode relevant morphological structure such as blebs/lobules or asymmetry.de_CH
dc.language.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectIntracranial aneurysmsde_CH
dc.subjectMorphologyde_CH
dc.subject.ddc616.8: Neurologie und Krankheiten des Nervensystemsde_CH
dc.titleIdentification of clinically relevant characteristics of intracranial aneurysm morphologyde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.conference.details1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedMedical Image Analysis and Data Modelingde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.funding.zhawAneuXde_CH
zhaw.author.additionalNode_CH
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Juchler, N., Schilling, S., Bijlenga, P., Rüfenacht, D., Kurtcuoglu, V., & Hirsch, S. (2019). Identification of clinically relevant characteristics of intracranial aneurysm morphology. 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019.
Juchler, N. et al. (2019) ‘Identification of clinically relevant characteristics of intracranial aneurysm morphology’, in 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
N. Juchler, S. Schilling, P. Bijlenga, D. Rüfenacht, V. Kurtcuoglu, and S. Hirsch, “Identification of clinically relevant characteristics of intracranial aneurysm morphology,” in 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019, 2019.
JUCHLER, Norman, Sabine SCHILLING, Philippe BIJLENGA, Daniel RÜFENACHT, Vartan KURTCUOGLU und Sven HIRSCH, 2019. Identification of clinically relevant characteristics of intracranial aneurysm morphology. In: 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 2019
Juchler, Norman, Sabine Schilling, Philippe Bijlenga, Daniel Rüfenacht, Vartan Kurtcuoglu, and Sven Hirsch. 2019. “Identification of Clinically Relevant Characteristics of Intracranial Aneurysm Morphology.” Conference poster. In 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019. ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Juchler, Norman, et al. “Identification of Clinically Relevant Characteristics of Intracranial Aneurysm Morphology.” 1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2019.


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