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Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

Multiple diffusion metrics in differentiating solid glioma from brain inflammation.

Kai Zhao¹, Ankang Gao¹, Eryuan Gao¹

¹Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Frontiers in Neuroscience

|February 14, 2024

Summary

The mean apparent propagator (MAP) model’s non-Gaussianity (NG) metric best differentiates brain inflammation from solid glioma. This diffusion-weighted imaging approach aids in diagnosing challenging MRI cases.

Keywords:

brain inflammation diffusion-weighted imaging glioma magnetic resonance imaging non-Gaussian

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Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

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Published on: September 13, 2022

Area of Science:

Neuroimaging
Radiology
Medical Diagnostics

Background:

Distinguishing solid glioma from brain inflammation on MRI is clinically significant but challenging.
Diffusion-weighted imaging (DWI) offers advanced metrics for tissue characterization.

Purpose of the Study:

To evaluate the efficacy of various diffusion metrics from multiple DWI models in differentiating solid glioma from brain inflammation.
To compare the diagnostic performance of different DWI models for this differential diagnosis.

Main Methods:

Prospective enrollment of 57 patients with glioma or inflammation and solid MRI lesions.
Acquisition of multi-b-value DWI data (0-2500 s/mm²).
Calculation of diffusion metrics using Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), Mean Apparent Propagator (MAP), and Neurite Orientation Dispersion and Density Imaging (NODDI) models.

Main Results:

The MAP model's non-Gaussianity (NG) metric demonstrated the highest diagnostic performance (AUC = 0.879).
Diffusion Kurtosis Imaging's mean kurtosis (MK) (AUC = 0.855) and NODDI's intracellular volume fraction (ICVF) (AUC = 0.825) also showed good performance.
Diffusion Tensor Imaging's mean diffusivity (MD) yielded the lowest diagnostic performance (AUC = 0.758).

Conclusions:

Multiple diffusion metrics derived from advanced DWI models can effectively differentiate between brain inflammation and solid glioma.
The MAP model, specifically its non-Gaussianity (NG) metric, shows superior performance for this differentiation.