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Glioma Imaging pp 241-249 | Cite as

Radiomics and Machine Learning

  • Julie Ferris
  • Peter D. Chang
  • Daniel S. ChowEmail author
Chapter

Abstract

Radiomics is an emerging field that attempts to quantitatively mine medical images for biomarkers including gene expression (imaging genomics or radiogenomics) that have clinical utility. While attempts have been made to visually decode various imaging features on MRIs of gliomas, an artificial intelligence approach is better suited to tease out pixel-level subtleties that may reflect different mutations. Machine learning, a form of artificial intelligence in which a computer learns what to look for without explicit human programming, has shown the most promise in the advancement of radiomics and imaging genomics for glioma characterization. Deep learning frameworks in particular have achieved high sensitivity and specificity in classifying MR images of gliomas by IDH1, 1p19q codeletion, and MGMT promoter methylation status. As these frameworks continue to improve, radiomics and imaging genomics could potentially serve a role in prognosticating outcomes and directing treatments for patients with gliomas.

Keywords

Radiomics Radiogenomics Imaging genomics Machine learning Deep learning Texture analysis Neural networks Artificial intelligence Glioblastoma Glioma 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Julie Ferris
    • 1
  • Peter D. Chang
    • 2
  • Daniel S. Chow
    • 2
    Email author
  1. 1.University of California, IrvineIrvineUSA
  2. 2.Department of RadiologyUniversity of California, IrvineIrvineUSA

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