Vertebral artery fusiform aneurysm geometry in predicting rupture risk.
Abstract
Cerebral aneurysms affect a significant portion of the adult population worldwide.
Despite significant progress, the development of robust techniques to evaluate the
risk of aneurysm rupture remains a critical challenge. We hypothesize that vertebral
artery fusiform aneurysm (VAFA) morphology may be predictive of rupture risk and can
serve as a deciding factor in clinical management. To investigate the VAFA morphology,
we use a combination of image analysis and machine learning techniques to study a
geometric feature set computed from a depository of 37 (12 ruptured and 25 un-ruptured)
aneurysm images. Of the 571 unique features we compute, we distinguish five features
for use by our machine learning classification algorithm by an analysis of statistical
significance. These machine learning methods achieve state-of-the-art classification
performance (81.43 ± 13.08%) for the VAFA morphology, and identify five features (cross-sectional
area change of aneurysm, maximum diameter of nearby distal vessel, solidity of aneurysm,
maximum curvature of nearby distal vessel, and ratio of curvature between aneurysm
and its nearby proximal vessel) as effective predictors of VAFA rupture risk. These
results suggest that the geometric features of VAFA morphology may serve as useful
non-invasive indicators for the prediction of aneurysm rupture risk in surgical settings.
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https://hdl.handle.net/10161/21200Published Version (Please cite this version)
10.1098/rsos.180780Publication Info
Zhao, Xiukun; Gold, Nathan; Fang, Yibin; Xu, Shixin; Zhang, Yongxin; Liu, Jianmin;
... Huang, Huaxiong (2018). Vertebral artery fusiform aneurysm geometry in predicting rupture risk. Royal Society open science, 5(10). pp. 180780. 10.1098/rsos.180780. Retrieved from https://hdl.handle.net/10161/21200.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Shixin Xu
Assistant Professor of Mathematics at Duke Kunshan University
Shixin Xu is an Assistant Professor of Mathematics. His research interests are machine
learning and data-driven model for diseases, multiscale modeling of complex fluids,
Neurovascular coupling, homogenization theory, and numerical analysis. The current
projects he is working on are
image data-based for the prediction of hemorrhagic transformation in acute ischemic
stroke,
electrodynamics modeling of saltatory conduction along myelina

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