Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

Detmer, Felicitas J.; Lückehe, Daniel; Mut, Fernando; Slawski, Martin; Hirsch, Sven; Bijlenga, Philippe; von Voigt, Gabriele; Cebral, Juan R.

doi:10.1007/s11548-019-02065-2

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Comparison of statistical learning approaches for cerebral aneurysm rupture assessment
Authors:	Detmer, Felicitas J. Lückehe, Daniel Mut, Fernando Slawski, Martin Hirsch, Sven Bijlenga, Philippe von Voigt, Gabriele Cebral, Juan R.
et. al:	No
DOI:	10.1007/s11548-019-02065-2
Published in:	International Journal of Computer Assisted Radiology and Surgery
Volume(Issue):	15
Issue:	1
Page(s):	141
Pages to:	150
Issue Date:	Jan-2020
Publisher / Ed. Institution:	Springer
ISSN:	1861-6410 1861-6429
Language:	English
Subjects:	Cerebral aneurysm; Hemodynamics; Machine learning; Prediction; Risk factor; Shape; Aneurysm, ruptured; Hemodynamics; Human; Intracranial aneurysm; ROC curve; Decision tree; Model, statistical; Support vector machine
Subject (DDC):	006: Special computer methods 616: Internal medicine and diseases
Abstract:	Purpose: 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.
Further description:	Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)
URI:	https://digitalcollection.zhaw.ch/handle/11475/27648
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
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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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.