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Updated: Mar 6, 2026

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

A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies.

P D Chang¹, H R Malone^2,3, S G Bowden^2,3

¹From the Departments of Radiology (P.D.C., L.H.S., A.L., J.G.).

AJNR. American Journal of Neuroradiology

|March 4, 2017

Summary

This summary is machine-generated.

This study developed a model correlating MRI signal intensity with glioblastoma cell density. This tool quantifies tumor infiltration, aiding surgical and radiation planning for better patient management.

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Last Updated: Mar 6, 2026

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

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Translational Orthotopic Models of Glioblastoma Multiforme

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Published on: February 17, 2023

Area of Science:

Neuro-oncology
Radiology
Computational Pathology

Background:

Glioblastoma exhibits complex MR imaging due to histopathologic heterogeneity.
Understanding signal correlations with infiltrating cells and edema is crucial for patient management.

Purpose of the Study:

To develop a predictive model for glioblastoma cellularity using MR signal intensity.
To quantify tumor infiltration and intratumoral heterogeneity across the entire tumor volume.

Main Methods:

Collected 91 localized biopsies from 36 glioblastoma patients.
Coregistered MR signal intensities (T1-postcontrast, T2-FLAIR, ADC) with biopsy locations.
Quantified cell density using automated cell-counting algorithms.

Main Results:

T2-FLAIR and ADC signal intensity showed inverse correlation with cell density (r = -0.61, r = -0.63).
T1-postcontrast subtraction showed direct correlation with cell density (r = 0.69).
A multiparametric model combining these sequences achieved improved correlation (r = 0.74).

Conclusions:

A quantitative model linking MR signal intensity to glioblastoma cell density was established.
This model enables mapping of intratumoral heterogeneity in both enhancing and non-enhancing regions.
The model can guide surgical resection, biopsy targeting, and radiation therapy planning.