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Int J Comput Assist Radiol Surg. 2019 Sep 4. doi: 10.1007/s11548-019-02065-2. [Epub ahead of print]

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.

Author information

1
Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA. fdetmer@gmu.edu.
2
Computational Health Informatics, Leibniz University, Hannover, Germany.
3
Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA.
4
Statistics Department, George Mason University, Fairfax, VA, USA.
5
Institute of Applied Simulation, ZHAW University of Applied Sciences, Wädenswil, Switzerland.
6
Neurosurgery, Clinical Neurosciences Department, Faculty of Medicine, University of Geneva, Geneva, Switzerland.

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.

KEYWORDS:

Cerebral aneurysm; Hemodynamics; Machine learning; Prediction; Risk factors; Shape

PMID:
31485987
DOI:
10.1007/s11548-019-02065-2

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