Distinguishing Molecular and Histologic Glioblastomas Using Multiparametric MRI-Based Habitat Analysis.

Area of Science:

• Neuro-oncology
• Radiology
• Biomedical Imaging

Background:

• Glioblastoma (GBM) is classified as either molecular (mol-GBM) or histological (hist-GBM).
• Understanding tumor heterogeneity is crucial for diagnosis and treatment.
• Multi-parametric physiologic MRI offers insights into tumor microenvironment characteristics.

Purpose of the Study:

• To investigate differences in tumor heterogeneity between mol-GBM and hist-GBM.
• To utilize multi-parametric physiologic MRI-based tumor habitat analysis for differentiation.
• To assess the diagnostic performance of MRI-derived features in distinguishing GBM subtypes.

Main Methods:

• Retrospective multi-institutional study analyzing MRI data from 52 patients (39 hist-GBM, 13 mol-GBM).
• Apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV), and volume transfer constant (Ktrans) were used to define eight spatial habitat clusters.
• Multivariable logistic regression models incorporating habitat features were developed and validated.

Main Results:

• Molecular GBM exhibited significantly lower proportions of high-grade malignant habitats (low ADC, high rCBV, high Ktrans) compared to hist-GBM.
• The mol-GBM group showed markedly lower total proportions of high Ktrans habitats.
• A habitat-based model achieved an AUC of 0.87 for internal validation and 88.9% accuracy for external validation.

Conclusions:

• Multiparametric MRI habitat analysis effectively reveals differences in tumor heterogeneity between mol-GBM and hist-GBM.
• Tumor permeability (Ktrans) is a key parameter for differentiating these GBM subtypes.
• Integrating MRI-derived habitat features with tumor volume may enhance the diagnostic accuracy for GBM subtypes.