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Updated: Jul 14, 2025

Translational Orthotopic Models of Glioblastoma Multiforme

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

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

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 genes Lower grade gliomas Magnetic resonance imaging Radiogenomic associations Spike-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.