Radiologic image-based statistical shape analysis of brain tumors
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018
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| Online Access: | https://eprints.nottingham.ac.uk/49379/ |
| _version_ | 1848797983943950336 |
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| author | Bharath, Karthik Kurtek, Sebastian Rao, Arvind Baladandayuthapani, Veerabhadran |
| author_facet | Bharath, Karthik Kurtek, Sebastian Rao, Arvind Baladandayuthapani, Veerabhadran |
| author_sort | Bharath, Karthik |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:12:33Z |
| format | Article |
| id | nottingham-49379 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:12:33Z |
| publishDate | 2018 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-493792019-03-15T04:30:17Z https://eprints.nottingham.ac.uk/49379/ Radiologic image-based statistical shape analysis of brain tumors Bharath, Karthik Kurtek, Sebastian Rao, Arvind Baladandayuthapani, Veerabhadran 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. Wiley 2018-11-30 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/49379/1/paperv5.pdf 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 Magnetic resonance imaging; Shape manifold; Glioblastoma multiforme; Clustering; Survival analysis http://onlinelibrary.wiley.com/doi/10.1111/rssc.12272/abstract doi:10.1111/rssc.12272 doi:10.1111/rssc.12272 |
| spellingShingle | Magnetic resonance imaging; Shape manifold; Glioblastoma multiforme; Clustering; Survival analysis Bharath, Karthik Kurtek, Sebastian Rao, Arvind Baladandayuthapani, Veerabhadran Radiologic image-based statistical shape analysis of brain tumors |
| title | Radiologic image-based statistical shape analysis of brain tumors |
| title_full | Radiologic image-based statistical shape analysis of brain tumors |
| title_fullStr | Radiologic image-based statistical shape analysis of brain tumors |
| title_full_unstemmed | Radiologic image-based statistical shape analysis of brain tumors |
| title_short | Radiologic image-based statistical shape analysis of brain tumors |
| title_sort | radiologic image-based statistical shape analysis of brain tumors |
| topic | Magnetic resonance imaging; Shape manifold; Glioblastoma multiforme; Clustering; Survival analysis |
| url | https://eprints.nottingham.ac.uk/49379/ https://eprints.nottingham.ac.uk/49379/ https://eprints.nottingham.ac.uk/49379/ |