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We began analyzing https://link.springer.com/chapter/10.1007/978-3-319-56820-1_6, but it redirected us to https://link.springer.com/chapter/10.1007/978-3-319-56820-1_6. The analysis below is for the second page.

Title[redir]:
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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Keywords {🔍}

pubmed, google, scholar, article, central, glioblastoma, cas, cancer, imaging, analysis, journal, rao, wang, data, tumor, chapter, molecular, features, van, ieee, research, radiology, radiogenomics, classification, medical, image, information, kim, radiomics, gliomas, zhang, biology, treatment, genomic, images, liu, survival, histomics, feature, multiforme, brain, genome, chen, aerts, informatics, privacy, cookies, content, publish, phenotypes,

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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 {🗺️}

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
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         name:Springer International Publishing
         logo:
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      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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Book:
      name:Advances in Biology and Treatment of Glioblastoma
      isbn:
         978-3-319-56820-1
         978-3-319-56819-5
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      name:Springer International Publishing
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         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
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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
      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
      name:Rice University
      address:
         name:Department of Electrical and Computer Engineering, Rice University, Houston, USA
         type:PostalAddress
      name:University of Texas MD Anderson Cancer Center
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         name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
         type:PostalAddress
      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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Person:
      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
      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
      name:Souptik Barua
      affiliation:
            name:Rice University
            address:
               name:Department of Electrical and Computer Engineering, Rice University, Houston, USA
               type:PostalAddress
            type:Organization
      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
      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
      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
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      email:[email protected]
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      name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
      name:Institute of Biosciences and Technology, Texas A&M University Health Science Center, Houston, USA
      name:Department of Electrical and Computer Engineering, Rice University, Houston, USA
      name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
      name:Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, USA
      name:Department of Bioinformatics and Computational Biology, Unit 1410, The University of Texas MD Anderson Cancer Center, Houston, USA
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