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Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue - 28/04/21

Doi : 10.1016/j.diii.2020.12.001 
Radwa K. Soliman a, , Abdelhakeem A. Essa b, Ahmed A.S. Elhakeem c, Sara A. Gamal a, Mohamed M.A. Zaitoun d
a Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Assiut University Hospitals, Asyut 71515, Egypt 
b Department of Neurosurgery, Assiut University Hospitals, Assiut 71515, Egypt 
c Department of Pathology, Faculty of Medicine, Al-Azhar University, Assiut 71515, Egypt 
d Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Zagazig University, Sharkia, Egypt 

Corresponding author.

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Highlights

Texture analysis derived from magnetic resonance imaging is a useful tool to quantify glioma heterogeneity.
Texture analysis parameters derived from apparent diffusion coefficient map are effective in predicting glioma grade.
Quantifying the heterogeneity of tumoral tissue and peri-tumoral edema with magnetic resonance imaging can yield additional important information for glioma grading.

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Abstract

Purpose

To prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG).

Materials and methods

A total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34–75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5–T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis.

Results

Mean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10−3 mm2/s and 1.21×10−3 mm2/s cut-off values, yielding 70% sensitivity each (95% CI: 59–82% and 61–80%, respectively), 80% (95% CI: 79–98%) and 90% (95% CI: 82–97%) specificity, and 73% (95% CI: 66–91%) and 76% (95% CI: 72–90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58–83%) and 70% (95% CI: 59–84%), specificities of 90% (95% CI: 80–95%) and 70% (95% CI: 64–83%) and accuracies of 79% (95% CI: 69–89%) and 70% (95% CI: 64–77%), respectively.

Conclusion

Measurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

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Keywords : Computer-assisted image analysis, Glioma, Tumor grading, Texture analysis, Diffusion magnetic resonance imaging

Abbreviations : ADC, AUC, CI, GLCM, HGG, ICC, LGG, MRI, OR, RLM, ROC, SD, SE, TA, VOIs, WHO


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© 2020  Société française de radiologie. Publicado por Elsevier Masson SAS. Todos los derechos reservados.
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Vol 102 - N° 5

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