Skip to main content

Research Repository

Advanced Search

DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Saha, Abhijoy; Banerjee, Sayantan; Kurtek, Sebastian; Narang, Shivali; Lee, Joonsang; Rao, Ganesh; Martinez, Juan; Bharath, Karthik; Baladandayuthapani, Veerabhadran

Authors

Abhijoy Saha

Sayantan Banerjee

Sebastian Kurtek

Shivali Narang

Joonsang Lee

Ganesh Rao

Juan Martinez

Veerabhadran Baladandayuthapani



Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

Citation

Saha, A., Banerjee, S., Kurtek, S., Narang, S., Lee, J., Rao, G., …Baladandayuthapani, V. (in press). DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer. NeuroImage: Clinical, 12, https://doi.org/10.1016/j.nicl.2016.05.012

Journal Article Type Article
Acceptance Date May 25, 2016
Online Publication Date May 27, 2016
Deposit Date Aug 15, 2017
Publicly Available Date Mar 29, 2024
Journal NeuroImage: Clinical
Electronic ISSN 2213-1582
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 12
DOI https://doi.org/10.1016/j.nicl.2016.05.012
Keywords Glioblastoma; Medical imaging; Tumor heterogeneity; Density estimation; Clustering; Fisher–Rao metric
Public URL https://nottingham-repository.worktribe.com/output/788405
Publisher URL http://www.sciencedirect.com/science/article/pii/S2213158216300882

Files





You might also like



Downloadable Citations