DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2016-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158216300882 |
| _version_ | 1811278951742439424 |
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| author | Abhijoy Saha Sayantan Banerjee Sebastian Kurtek Shivali Narang Joonsang Lee Ganesh Rao Juan Martinez Karthik Bharath Arvind U.K. Rao Veerabhadran Baladandayuthapani |
| author_facet | Abhijoy Saha Sayantan Banerjee Sebastian Kurtek Shivali Narang Joonsang Lee Ganesh Rao Juan Martinez Karthik Bharath Arvind U.K. Rao Veerabhadran Baladandayuthapani |
| author_sort | Abhijoy Saha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:46:00Z |
| format | Article |
| id | doaj.art-df69e0727bf54758871b8b0b9de7ae15 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-04-13T00:46:00Z |
| publishDate | 2016-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-df69e0727bf54758871b8b0b9de7ae152022-12-22T03:10:01ZengElsevierNeuroImage: Clinical2213-15822016-01-0112C13214310.1016/j.nicl.2016.05.012DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancerAbhijoy Saha0Sayantan Banerjee1Sebastian Kurtek2Shivali Narang3Joonsang Lee4Ganesh Rao5Juan Martinez6Karthik Bharath7Arvind U.K. Rao8Veerabhadran Baladandayuthapani9Department of Statistics, The Ohio State University, United StatesOperations Management and Quantitative Techniques Area, Indian Institute of Management Indore, IndiaDepartment of Statistics, The Ohio State University, United StatesDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, United StatesDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, United StatesDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, United StatesDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, United StatesSchool of Mathematical Sciences, The University of Nottingham, United KingdomDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, United StatesDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, United StatesTumor 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.http://www.sciencedirect.com/science/article/pii/S2213158216300882GlioblastomaMedical imagingTumor heterogeneityDensity estimationClusteringFisher–Rao metric |
| spellingShingle | Abhijoy Saha Sayantan Banerjee Sebastian Kurtek Shivali Narang Joonsang Lee Ganesh Rao Juan Martinez Karthik Bharath Arvind U.K. Rao Veerabhadran Baladandayuthapani DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer NeuroImage: Clinical Glioblastoma Medical imaging Tumor heterogeneity Density estimation Clustering Fisher–Rao metric |
| title | DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer |
| title_full | DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer |
| title_fullStr | DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer |
| title_full_unstemmed | DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer |
| title_short | DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer |
| title_sort | demarcate density based magnetic resonance image clustering for assessing tumor heterogeneity in cancer |
| topic | Glioblastoma Medical imaging Tumor heterogeneity Density estimation Clustering Fisher–Rao metric |
| url | http://www.sciencedirect.com/science/article/pii/S2213158216300882 |
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