Radiologic Image-Based Statistical Shape Analysis of Brain Tumors

• Activity Number: 37 - Object-Oriented Analysis of Imaging Data
• Type: Contributed
• Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Sebastian Kurtek* and Karthik Bharath and Arvind Rao and Veera Baladandayuthapani
• Companies: Ohio State University and University of Nottingham and University of Michigan and University of Michigan
• Keywords: clustering; glioblastoma multiforme; magnetic resonance imaging; shape manifold; survival analysis

Abstract

We apply 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 enables comparisons of tumor shapes, provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumor shapes and 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, tumor subtype and genomic characteristics. Furthermore, it is demonstrated that adding tumor shape information to survival models containing clinical and genomic variables results in a significant increase in predictive power.