Radiologic image-based statistical shape analysis of brain tumors

Bharath, Karthik, Kurtek, Sebastian, Rao, Arvind and Baladandayuthapani, Veerabhadran (2018) Radiologic image-based statistical shape analysis of brain tumors. Journal of the Royal Statistical Society: Series C, 67 (5). pp. 1357-1378. ISSN 1467-9876

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We propose a curve-based Riemannian-geometric approach for general shape-based statistical analyses of tumors obtained from radiologic images. A key component of the framework is a suitable metric that (1) enables comparisons of tumor shapes, (2) provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumor shapes, and (3) allows for a rich class of continuous deformations of a tumor shape. The utility of the framework is illustrated through specific statistical tasks on a dataset of radiologic images of patients diagnosed with glioblastoma multiforme, a malignant brain tumor with poor prognosis. In particular, our analysis discovers two patient clusters with very different survival, subtype and genomic characteristics. Furthermore, it is demonstrated that adding tumor shape information into survival models containing clinical and genomic variables results in a significant increase in predictive power.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Bharath, K., Kurtek, S., Rao, A. and Baladandayuthapani, V. (2018), Radiologic image-based statistical shape analysis of brain tumours. J. R. Stat. Soc. C. doi:10.1111/rssc.12272, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Keywords: Magnetic resonance imaging; Shape manifold; Glioblastoma multiforme; Clustering; Survival analysis
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
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Depositing User: Bharath, Karthik
Date Deposited: 29 Jan 2018 14:08
Last Modified: 15 Mar 2019 04:30

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