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Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging.
Johann-Martin Hempel1,2, Cornelia Brendle1,2, Sasan Darius Adib2,3
1Department of Neuroradiology, University Hospital Tübingen, Eberhard Karls University, 72076 Tübingen, Germany.
Journal of Clinical Medicine
|June 2, 2021
Summary
A unique combination of mean kurtosis (MK) and mean diffusivity (MD) values from diffusion kurtosis imaging (DKI) can identify diffuse glioma. This diffusion signature aids in automatic tumor segmentation and understanding tumor heterogeneity.
Area of Science:
- Neuroimaging
- Radiology
- Oncology
Background:
- Gliomas are primary brain tumors with diverse characteristics.
- Accurate glioma grading and segmentation are crucial for treatment planning.
- Diffusion Kurtosis Imaging (DKI) offers advanced insights into tissue microstructure.
Purpose of the Study:
- To evaluate the relationship between mean kurtosis (MK) and mean diffusivity (MD) values in glioma.
- To determine if specific MK and MD combinations can identify gliomas.
- To explore the potential of these diffusion parameters for automated tumor detection.
Main Methods:
- Retrospective analysis of 77 treatment-naïve glioma patients.
- Whole-brain DKI parametric maps were generated from preoperative MR images.
- Scatter plot analysis identified unique MK-MD value combinations in glioma tissue.
Main Results:
- A distinct MK-MD value combination was exclusively found in glioma tissue, including the infiltrative zone.
- This specific diffusion signature was absent in normal brain tissue and other intracranial compartments.
- The identified voxels showed spatial overlap with manually segmented tumor volumes.
Conclusions:
- A unique diffusion signature (MK-MD combination) from whole-brain DKI can identify diffuse glioma without prior segmentation.
- This finding may enhance artificial intelligence algorithms for automatic glioma segmentation.
- The diffusion signature provides novel insights into glioma heterogeneity.
