Predicting the risk of rupture for vertebral aneurysm based on geometric features of blood vessels

Abstract

A significant proportion of the adult population worldwide suffers from cerebral aneurysms. If left untreated, aneurysms may rupture and lead to fatal massive internal bleeding. On the other hand, treatment of aneurysms also involve significant risks. It is desirable, therefore, to have an objective tool that can be used to predict the risk of rupture and assist in surgical decision for operating on the aneurysms. Currently, such decisions are made mostly based on medical expertise of the healthcare team. In this paper, we investigate the possibility of using machine learning algorithms to predict rupture risk of vertebral artery fusiform aneurysms based on geometric features of the blood vessels surrounding but excluding the aneurysm. For each of the aneurysm images (12 ruptured and 25 unruptured), the vessel is segmented into distal and proximal parts by cross-sectional area and 382 non-aneurysm-related geometric features extracted. The decision tree model using two of the features (standard deviation of eccentricity of proximal vessel, and diameter at the distal endpoint) achieved 83.8% classification accuracy. Additionally, with support vector machine and logistic regression, we also achieved 83.8% accuracy with another set of two features (ratio of mean curvature between distal and proximal parts, and diameter at the distal endpoint). Combining the aforementioned three features with integration of curvature of proximal vessel and also ratio of mean cross-sectional area between distal and proximal parts, these models achieve an impressive 94.6% accuracy. These results strongly suggest the usefulness of geometric features in predicting the risk of rupture.

Type
Publication
Royal Society Open Science (8)
Arvind Gupta
Arvind Gupta
Professor

Arvind Gupta is Professor of Computer Science at the University of Toronto. He has served as President and Vice-Chancellor of UBC, and as the CEO and Scientific Director of Mitacs Inc. He is also a founder of Palette Skills Inc. He has published extensively on theoretical computer science, computational genomics and national innovation strategies.

Shixin Xu
Shixin Xu
Assistant Professor

Shixin Xu is an Assistant Professor of Mathematics at Duke Kunshan University. His research interests are machine learning and data-driven models for diseases, multiscale modeling of complex fluids, homogenization theory, and numerical analysis. Xu has a B.Sc. in mathematics (honors) from Ocean University of China and a Ph.D. in mathematics from the University of Science and Technology China. From 2013 to 2017, he held postdoctoral positions at the National University of Singapore, the University of Notre Dame, the University of California, Riverside, and the Fields Institute for Research in Mathematical Sciences, Canada.

Huaxiong Huang
Huaxiong Huang
Professor

Huaxiong Huang is Professor of Mathematics at the York University. He is VP (Academic) and Executive Director of Research Center of Mathematics (Zhuhai, China). He has served as Deputy Director of the Fields Institute and Director of the Fields Centre for Quantitative Analysis and Modelling. His wide array of publications in applied mathematics focus on fluid mechanics and scientific computing, finance, biology, physiology, energy and medicine.