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Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
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A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies
P D Chang1, H R Malone2,3, S G Bowden2,3
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 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.
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.
