Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas

Area of Science:

• Neuroimaging
• Radiology
• Oncology

Background:

• Gliomas are primary brain tumors with grading crucial for prognosis and treatment.
• Current grading methods can be subjective and time-consuming.
• Advanced diffusion MRI techniques offer potential for objective tumor characterization.

Purpose of the Study:

• To evaluate the effectiveness of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.
• To assess the significance of tumor heterogeneity measures derived from DTI and DKI for glioma classification.
• To explore the utility of a novel volume heterogeneity index for automated glioma grading.

Main Methods:

• Retrospective analysis of 53 glioma patients (WHO grades II, III, IV).
• Calculation of texture measures (entropy, busyness) and a volume heterogeneity index from DTI and DKI parametric maps.
• Statistical comparison of measures between grades using Mann-Whitney test and ROC analysis.

Main Results:

• Texture measures and volume heterogeneity index showed significant differences across glioma grades.
• Mean diffusivity (MD) and mean kurtosis (MK) exhibited high discriminability between grades.
• Volume heterogeneity index demonstrated strong correlations with glioma grade, with lower grades showing more volume homogeneity.

Conclusions:

• Tumor heterogeneity analysis using DTI and DKI parameters shows promise for automating glioma grading.
• These imaging biomarkers can objectively differentiate between glioma grades.
• Further validation may lead to improved diagnostic accuracy and treatment planning.