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Title: Radiologic Image-Based Statistical Shape Analysis of Brain Tumours
Summary

We propose a curve-based Riemannian geometric approach for general shape-based statistical analyses of tumours obtained from radiologic images. A key component of the framework is a suitable metric that enables comparisons of tumour shapes, provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumour shapes and allows for a rich class of continuous deformations of a tumour shape. The utility of the framework is illustrated through specific statistical tasks on a data set of radiologic images of patients diagnosed with glioblastoma multiforme, a malignant brain tumour 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 tumour shape information to survival models containing clinical and genomic variables results in a significant increase in predictive power.

 
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Award ID(s):
1740761 1613054
NSF-PAR ID:
10398887
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Journal of the Royal Statistical Society Series C: Applied Statistics
Volume:
67
Issue:
5
ISSN:
0035-9254
Page Range / eLocation ID:
p. 1357-1378
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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