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dc.contributor.authorDetmer, Felicitas J.-
dc.contributor.authorLückehe, Daniel-
dc.contributor.authorMut, Fernando-
dc.contributor.authorSlawski, Martin-
dc.contributor.authorHirsch, Sven-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorvon Voigt, Gabriele-
dc.contributor.authorCebral, Juan R.-
dc.date.accessioned2023-04-19T13:51:29Z-
dc.date.available2023-04-19T13:51:29Z-
dc.date.issued2020-01-
dc.identifier.issn1861-6410de_CH
dc.identifier.issn1861-6429de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27648-
dc.descriptionErworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)de_CH
dc.description.abstractPurpose: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status. Methods: Hemodynamic, morphological, and patient-related information of 1631 cerebral aneurysms characterized by computational fluid dynamics simulations were used to train support vector machines (SVMs) with linear and RBF kernel (RBF-SVM), k-nearest neighbors (kNN), decision tree, random forest, and multilayer perceptron (MLP) neural network classifiers for predicting the aneurysm rupture status. The classifiers’ accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated and compared to the LRM using 249 test cases obtained from two external cohorts. Additionally, important variables were determined based on the random forest and weights of the linear SVM. Results: The AUCs of the MLP, LRM, linear SVM, RBF-SVM, kNN, decision tree, and random forest were 0.83, 0.82, 0.80, 0.81, 0.76, 0.70, and 0.79, respectively. The accuracy ranged between 0.76 (decision tree,) and 0.79 (linear SVM, RBF-SVM, and MLP). Important variables for predicting the aneurysm rupture status included aneurysm location, the mean surface curvature, and maximum flow velocity. Conclusion: The performance of the LRM was overall comparable to that of the other ML classifiers, confirming its potential for aneurysm rupture assessment. To further improve the predictions, additional information, e.g., related to the aneurysm wall, might be needed.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofInternational Journal of Computer Assisted Radiology and Surgeryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCerebral aneurysmde_CH
dc.subjectHemodynamicsde_CH
dc.subjectMachine learningde_CH
dc.subjectPredictionde_CH
dc.subjectRisk factorde_CH
dc.subjectShapede_CH
dc.subjectAneurysm, rupturedde_CH
dc.subjectHemodynamicsde_CH
dc.subjectHumande_CH
dc.subjectIntracranial aneurysmde_CH
dc.subjectROC curvede_CH
dc.subjectDecision treede_CH
dc.subjectModel, statisticalde_CH
dc.subjectSupport vector machinede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleComparison of statistical learning approaches for cerebral aneurysm rupture assessmentde_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.doi10.1007/s11548-019-02065-2de_CH
dc.identifier.pmid31485987de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end150de_CH
zhaw.pages.start141de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume15de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.funding.zhawAneuXde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Detmer, F. J., Lückehe, D., Mut, F., Slawski, M., Hirsch, S., Bijlenga, P., von Voigt, G., & Cebral, J. R. (2020). Comparison of statistical learning approaches for cerebral aneurysm rupture assessment. International Journal of Computer Assisted Radiology and Surgery, 15(1), 141–150. https://doi.org/10.1007/s11548-019-02065-2
Detmer, F.J. et al. (2020) ‘Comparison of statistical learning approaches for cerebral aneurysm rupture assessment’, International Journal of Computer Assisted Radiology and Surgery, 15(1), pp. 141–150. Available at: https://doi.org/10.1007/s11548-019-02065-2.
F. J. Detmer et al., “Comparison of statistical learning approaches for cerebral aneurysm rupture assessment,” International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 1, pp. 141–150, Jan. 2020, doi: 10.1007/s11548-019-02065-2.
DETMER, Felicitas J., Daniel LÜCKEHE, Fernando MUT, Martin SLAWSKI, Sven HIRSCH, Philippe BIJLENGA, Gabriele VON VOIGT und Juan R. CEBRAL, 2020. Comparison of statistical learning approaches for cerebral aneurysm rupture assessment. International Journal of Computer Assisted Radiology and Surgery. Januar 2020. Bd. 15, Nr. 1, S. 141–150. DOI 10.1007/s11548-019-02065-2
Detmer, Felicitas J., Daniel Lückehe, Fernando Mut, Martin Slawski, Sven Hirsch, Philippe Bijlenga, Gabriele von Voigt, and Juan R. Cebral. 2020. “Comparison of Statistical Learning Approaches for Cerebral Aneurysm Rupture Assessment.” International Journal of Computer Assisted Radiology and Surgery 15 (1): 141–50. https://doi.org/10.1007/s11548-019-02065-2.
Detmer, Felicitas J., et al. “Comparison of Statistical Learning Approaches for Cerebral Aneurysm Rupture Assessment.” International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 1, Jan. 2020, pp. 141–50, https://doi.org/10.1007/s11548-019-02065-2.


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