Evaluation of Diffusion Anisotropy and Diffusion Shape in Grading of Glial Tumors

F Davanian, F Faeghi, S Shahzadi, Z Farshidfar


Background: The most common primary tumors of brain are gliomas. Grading of tumor is vital for designing proper treatment plans. The gold standard choice to determine the grade of glial tumor is biopsy which is an invasive method.

Objective: In this study, we try to investigate the role of fractional anisotropy (diffusion anisotropy) and linear anisotropy coefficient (its shape) with the aim of Diffusion Tensor imaging (as a non-invasive method) in the grading of gliomas.

Methods: A group of 20 patients with histologically glial approved was evaluated. In this study, we used a 1.5-Tesla MR system (AVANTO; Siemens, Germany) with a standard head coil for scanning. Multi-directional diffusion weighted imaging (measured in 12 non-collinear directions) and T1 weighted non-enhanced were performed for all patients. We defined two Regions of Interest (ROIs); white matter adjacent to the tumor and the homologous fiber tracts to the first ROI in the contralateral hemisphere.

Results: Linear anisotropy coefficient (CL), fractional anisotropy (FA) values and ratios of low-grade peri-tumoral fiber tracts were higher than high-grade gliomas (P-value CLt=0.014, P-value CLt/n=0.019 and P-value FAt=0.006, P-value FAt/n=0.024).In addition, we perform ROC curve for each parameter (CL ratio-AUC = 0.82and FA ratio-AUC = 0.868).

Conclusion: Our findings prove significant difference between diffusion anisotropy (FA) and diffusion shape (Cl) between low grade and high grade glioma, based on which we find this evaluation helpful in the grading of glial tumors.


Diffusion Magnetic Resonance Imaging ● Glioma ● Anisotropy

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DOI: https://doi.org/10.31661/jbpe.v0i0.513

eISSN: 2251-7200        JBPE NLM ID: 101589641

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                                                                        Chairman and Editor in Chief

                                                                              Dr. Alireza Mehdizadeh

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