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Tumor radiogenomics in gliomas with Bayesian layered variable selection.

Shariq Mohammed1, Sebastian Kurtek2, Karthik Bharath3

  • 1Department of Biostatistics, Boston University, 801 Massachusetts Ave, Boston, MA 02118, United States; Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48103, United States; Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, United States.

Medical Image Analysis
|October 5, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new statistical method to link brain tumor imaging features with genetic data in lower grade gliomas (LGG). This approach identifies potential early diagnostic markers for disease monitoring.

Keywords:
Cancer driver genesLower grade gliomasMagnetic resonance imagingRadiogenomic associationsSpike-and-slab prior

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Area of Science:

  • Radiology
  • Genomics
  • Statistical Modeling

Background:

  • Lower grade gliomas (LGG) are heterogeneous brain tumors.
  • Understanding radiogenomic associations is crucial for diagnosis and treatment.
  • Current methods may not fully capture tumor complexity.

Purpose of the Study:

  • To develop a statistical framework for analyzing radiogenomic associations in LGG.
  • To identify novel imaging phenotypes reflecting tumor evolution.
  • To link these phenotypes with genomic markers.

Main Methods:

  • A novel imaging phenotype using concentric spherical layers of MRI data.
  • Representation of MRI data using probability density functions and Riemannian geometry.
  • Bayesian variable selection models with hierarchical priors.
  • Expectation-Maximization algorithm for efficient estimation.

Main Results:

  • The proposed framework effectively identifies radiogenomic associations in LGG.
  • Simulation studies show superior performance compared to existing methods.
  • Identified genes associated with tumor layers are linked to survival and oncogenesis.

Conclusions:

  • The framework provides a powerful tool for radiogenomic analysis in LGG.
  • Identified associations may serve as early diagnostic markers.
  • The R package facilitates the application of this framework.