Literature DB >> 27562582

Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas.

Lamiaa El-Serougy1, Ahmed Abdel Khalek Abdel Razek2, Amani Ezzat1, Hany Eldawoody3, Ahmad El-Morsy1.   

Abstract

AIM: The aim of this article is to assess diffusion tensor imaging (DTI) metrics in differentiating low-grade from high-grade gliomas. PATIENTS AND METHODS: A prospective study was conducted on 35 patients with gliomas who underwent DTI. Gliomas were classified into low-grade and high-grade gliomas. The fractional anisotropy (FA), mean diffusivity (MD), linear coefficient (CL), planar coefficient (CP) and spherical coefficient (CS) of the solid tumoral part and peri-tumoral regions were calculated.
RESULTS: There was significant difference (p = 0.001) in MD of the solid tumoral part of low-grade (1.78 ± 0.33 × 10(-3 )mm(2)/s) and high-grade (1.16 ± 0.22 × 10(-3 )mm(2)/s) gliomas. The selection of 1.42 × 10(-3 )mm(2)/s as a cutoff value of MD of the tumoral part was used to differentiate low-grade and high-grade gliomas; the best results were obtained with area under the curve (AUC) of 0.957 and accuracy of 91.4%. There was a significant difference in FA, MD, CP and CS of peri-tumoral regions of both groups with p values of 0.006, 0.042, 0.030 and 0.037, respectively. The cutoff values of MD, FA, CS and CP of the peri-tumoral region used to differentiate low-grade from high-grade gliomas were 1.24, 0.315, 0.726 and 0.321 with AUC of 0.694, 0.773, 0.734 and 0.724 and accuracy of 68.6%, 80.0%, 74.3% and 74.3%, respectively. The combined MD of the solid tumoral part and FA of the peri-tumoral region used to differentiate low-grade from high-grade gliomas revealed AUC of 0.974 and accuracy of 88.6%.
CONCLUSION: We conclude that the combination of MD of the solid tumoral part and FA of the peri-tumoral region is a noninvasive method to differentiate low-grade from high-grade gliomas.
© The Author(s) 2016.

Entities:  

Keywords:  Diffusion tensor imaging; glioma; grading

Mesh:

Year:  2016        PMID: 27562582      PMCID: PMC5033100          DOI: 10.1177/1971400916665382

Source DB:  PubMed          Journal:  Neuroradiol J        ISSN: 1971-4009


  39 in total

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  40 in total

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