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.
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Notes
Segmentations of the raw 3D-DRA images for the AneuX dataset were performed using the Aneufuse platform. Data are stored at the Swiss Institute of Bioinformatics and available to the scientific community by written request at adb@itis.ethz.ch.
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Acknowledgements
The authors would like to thank Olivier Brina, Norman Juchler, Rafik Ouared, Diana Sapina, and Mari Cruz Villa-Uriol for helping with the collection and image segmentation of the AneuX data.
Funding
SH and PB were supported by SystemsX.ch project AneuX evaluated by the Swiss National Science Foundation. Data for the AneuX dataset was collected and processed in the context of the @neurIST project funded by the EU commission (IST-2004-027703) and AneuX project evaluated by the Swiss National Science Foundation and funded by the SystemsX.ch initiative (MRD 2014/261).
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Raw 3D-DRA of the AneuX test data set were provided by the University Hospital of Geneva and collected with formal patient consent according to the @neurIST protocol and ethics authorization PB_2018-00073 (previously CER 07-05) released June 1st 2007 and renewed April 13th 2010, August 19th 2014 and February 28th 2018 initially by the Geneva Cantonal Ethics Commission for Research involving Humans and renewed by Swissethics in 2018.
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Detmer, F.J., Lückehe, D., Mut, F. et al. Comparison of statistical learning approaches for cerebral aneurysm rupture assessment. Int J CARS 15, 141–150 (2020). https://doi.org/10.1007/s11548-019-02065-2
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DOI: https://doi.org/10.1007/s11548-019-02065-2