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Title:
Radiogenomics and Histomics in Glioblastoma: The Promise of Linking Image-Derived Phenotype with Genomic Information | SpringerLink
Description:
Intra-tumor heterogeneity is the fundamental challenge in finding a cure for late-stage cancers. Physical biopsies do not sufficiently cover the diversity of molecular phenotypes within the tumor. Treatments are only effective on a subset of vulnerable tumor cells...
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edema/cellular invasion mri-phenotypes tumor-derived mri-texture features month download article/chapter mri-derived volumetric features linking image-derived phenotype semi-automatic morphometric analysis 18f-fdg pet/ct tcga research network image-derived phenotype data ct-based volumetric features 18f-fdopa pet/ct machine-based morphologic analysis central nervous system diffuse lower-grade gliomas genome sequencing centers multi-modal glioblastoma segmentation measure treatment response current cancer research google scholar device instant download digital pathology images privacy choices/manage cookies integrative genomic analysis mr imaging correlates genomic information chapter high-grade gliomas small research groups multi-institutional study arvind rao phd [18f] fluorodeoxyglucose pet cellular expression patterns gene expression data sparse feature learning machine learning applications brain tumor type editor information editors principal component analysis tcga-gbm dataset enabling models built compute cellular morphometry convolutional neural networks glioma groups based local representative tiles messenger rna expression magnetic resonance imaging identify imaging phenotypes quantitative image features characterize disease phenotypes somatic genomic landscape radiologic risk models
Schema {πΊοΈ}
ScholarlyArticle:
headline:Radiogenomics and Histomics in Glioblastoma: The Promise of Linking Image-Derived Phenotype with Genomic Information
pageEnd:159
pageStart:143
image:https://media.springernature.com/w153/springer-static/cover/book/978-3-319-56820-1.jpg
genre:
Medicine
Medicine (R0)
isPartOf:
name:Advances in Biology and Treatment of Glioblastoma
isbn:
978-3-319-56820-1
978-3-319-56819-5
type:Book
publisher:
name:Springer International Publishing
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
type:Organization
author:
name:Michael Lehrer
affiliation:
name:University of Texas MD Anderson Cancer Center
address:
name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
type:PostalAddress
type:Organization
type:Person
name:Reid T. Powell
affiliation:
name:Texas A&M University Health Science Center
address:
name:Institute of Biosciences and Technology, Texas A&M University Health Science Center, Houston, USA
type:PostalAddress
type:Organization
type:Person
name:Souptik Barua
affiliation:
name:Rice University
address:
name:Department of Electrical and Computer Engineering, Rice University, Houston, USA
type:PostalAddress
type:Organization
type:Person
name:Donnie Kim
affiliation:
name:University of Texas MD Anderson Cancer Center
address:
name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
type:PostalAddress
type:Organization
type:Person
name:Shivali Narang
affiliation:
name:University of Texas MD Anderson Cancer Center
address:
name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
type:PostalAddress
type:Organization
type:Person
name:Arvind Rao
affiliation:
name:Unit 1410, The University of Texas MD Anderson Cancer Center
address:
name:Department of Bioinformatics and Computational Biology, Unit 1410, The University of Texas MD Anderson Cancer Center, Houston, USA
type:PostalAddress
type:Organization
email:[email protected]
type:Person
keywords:Radiomics, Radiogenomics, Histomics, Glioma, Glioblastoma, Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography
description:Intra-tumor heterogeneity is the fundamental challenge in finding a cure for late-stage cancers. Physical biopsies do not sufficiently cover the diversity of molecular phenotypes within the tumor. Treatments are only effective on a subset of vulnerable tumor cells due to the prevalence of tumor stem-like cells. GBM tumors exemplify these general properties of late-stage cancers, with heterogeneous molecular profiles, histology, and radiology. Radiomics aims to characterize disease phenotypes from radiology scans in order to provide an alternative view of tumor heterogeneity, enabling models built from retrospective analysis of radiology scan data, and their integration with clinical data and molecular profiles. Computational histology (histomics) follows a workflow analogous to that of radiomics, with pre-processing, segmentation, feature extraction and analytics. The goal of histomics is to compute cellular morphometry and heterogeneity features from histology datasets. Genomic traits can potentially be inferred from histologic features by analysis of large, linked pathology-genomic data sets. There is also an active investigation of computer vision and machine learning applications to classify gliomas using radiology and histology imagesGlioma
radiology and histology images
. The potential of radiomics, radiogenomics and histomics studies is to advance personalized cancer treatment by enabling interpretation of biological mechanisms underlying imaging phenotypes. These efforts aim to make personalized therapies more accessible. Results from preliminary imaging could direct administration of precision assays to guide treatment, measure treatment response and identify targetable genetic alterations from image-derived phenotype data, across biological scale. Radiomics and histomics promises to revolutionize the practice of personalized medicine, by providing an important complement to molecular strategies.
datePublished:2017
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name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
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name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
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name:Shivali Narang
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name:University of Texas MD Anderson Cancer Center
address:
name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
type:PostalAddress
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name:Arvind Rao
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name:Unit 1410, The University of Texas MD Anderson Cancer Center
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name:Department of Bioinformatics and Computational Biology, Unit 1410, The University of Texas MD Anderson Cancer Center, Houston, USA
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name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
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