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Related Experiment Video

Updated: Apr 29, 2026

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
05:45

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Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.

Olivier Gevaert1, Lex A Mitchell, Achal S Achrol

  • 1From the Departments of Medicine (O.G.), Radiology (O.G., L.A.M., J.X., S.E., S.N., G.Z., S.K.P.), and Neurosurgery (A.S.A., G.K.S., S.H.C.), Stanford University, 1265 Welch Rd, Stanford, CA, 94304-5479.

Radiology
|May 16, 2014
PubMed
Summary

Quantitative MRI features can noninvasively predict glioblastoma multiforme (GBM) characteristics. Radiogenomic mapping links image features to molecular pathways, offering insights into tumor biology and patient outcomes.

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Area of Science:

  • Radiology
  • Oncology
  • Bioinformatics

Background:

  • Glioblastoma multiforme (GBM) is an aggressive brain tumor.
  • Understanding GBM's radiographic and molecular heterogeneity is crucial for treatment.

Purpose of the Study:

  • Derive quantitative MR imaging features to characterize GBM radiographic phenotype.
  • Create radiogenomic maps linking image features with molecular data.

Main Methods:

  • Extracted quantitative image features from MRIs of 55 GBM patients.
  • Identified robust features using intraclass correlation and test-retest analysis.
  • Correlated features with survival, radiologist annotations (VASARI), and molecular subgroups.

Main Results:

  • 18 robust image features identified, totaling 54 across ROIs.
  • Three features correlated with survival; seven with molecular subgroups.
  • 56% of image features linked to biological processes via radiogenomic maps.

Conclusions:

  • Radiogenomic analysis noninvasively predicts GBM clinical and molecular traits.
  • This approach enhances understanding of tumor heterogeneity.
  • Potential for improved diagnostic and prognostic capabilities in GBM.