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Updated: Nov 28, 2025

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

Estimating Local Cellular Density in Glioma Using MR Imaging Data.

E D H Gates^1,2, J S Weinberg³, S S Prabhu³

¹From the Departments of Imaging Physics (E.D.H.G., J.S.L., J.D.H., D.T.F.).

AJNR. American Journal of Neuroradiology

|November 27, 2020

Summary

This summary is machine-generated.

Machine learning models can estimate glioma cellular density from MR imaging. This technique offers spatially specific insights for improved diagnosis and treatment planning.

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Area of Science:

Neuro-oncology
Medical Imaging
Machine Learning

Background:

Gliomas exhibit increased cellular density in both tumor bulk and infiltration zones.
Altered cellular density affects imaging findings, but quantitative estimation from imaging remains challenging.

Purpose of the Study:

To identify optimal MR imaging and processing techniques for quantitative, spatially specific cellular density estimation in gliomas.

Main Methods:

Prospective collection of preoperative MR imaging (anatomic, diffusion, perfusion, permeability) and stereotactic biopsy histopathology from glioma patients.
Application of machine learning methodologies to estimate cellular density from MR image intensity data, using biopsy measurements as the ground truth.

Main Results:

Random forest models achieved R² = 0.59 using four imaging sequences (T2, fractional anisotropy, CBF, permeability AUC).
Conventional MR imaging inputs (T1, T2, FLAIR) resulted in slightly lower performance (R² = 0.52).
Spatially specific cellular density maps were generated as outputs.

Conclusions:

Moderate-to-strong correlations exist between MR imaging inputs and cellular density.
Random forest machine learning provides the most accurate estimates for glioma cellular density.
Spatially specific cellular density estimations hold potential for guiding glioma diagnosis and treatment.