Differentiation of glioma malignancy grade using diffusion MRI.

Area of Science:

• Neuroimaging
• Biophysics
• Oncology

Background:

• Diffusion MRI protocols now enable multi-shell data acquisition with high diffusion weightings within clinical timeframes.
• Accurate grading of human brain gliomas is crucial for treatment planning and prognosis.

Purpose of the Study:

• To evaluate diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) as potential biomarkers for human brain glioma grade differentiation.
• To assess the efficacy of these diffusion MRI techniques using a single, clinically feasible diffusion protocol.

Main Methods:

• Acquisition of multi-shell diffusion MRI data with b-values of 0, 1000, and 2500 s/mm² and 60 diffusion directions.
• Analysis of three diffusion approaches: DTI, DKI, and NODDI.
• Inclusion of 8 subjects per glioma grade group (II, III, and IV).

Main Results:

• DKI and NODDI scalar metrics demonstrated significant differences (p<0.05) in differentiating glioma grades, particularly between glioma II vs. III and glioma III vs. IV.
• Mean kurtosis and orientation dispersion index emerged as key metrics for reliable glioma grading.
• Example values: Mean kurtosis (0.31, 0.51, 0.90) and orientation dispersion index (0.14, 0.30, 0.59) for glioma grades II, III, and IV, respectively.

Conclusions:

• DKI and NODDI metrics are effective biomarkers for distinguishing human brain glioma grades.
• These diffusion MRI models, based on intra-/extra-axonal compartmentalization, offer promising criteria for glioma grading.
• Further investigation into the limitations and perspectives of these biophysical models is warranted.