Texture analysis of computed tomography images in characterization of oral cancers involving buccal mucosa. Download paper 
Objective: The aim of this study was to investigate the usefulness of texture analysis in the characterization of oral cancers involving the buccal mucosa and to assess its effectiveness in differentiating between the various grades of the tumour.
Methods: Contrast enhanced CT examination was carried out in 21 patients with carcinoma of the buccal mucosa who had consented to retrospective analysis during a research study that was approved by the institutional review board. Two regions of interest (ROIs) were
created, one at the site of the lesion and the other at the contralateral normal side. Texture analysis measures of fractal dimension (FD), lacunarity and grey level co-occurrence matrix (GLCM) were computed for each ROI. The numeric data from the two ROIs were compared and were correlated with the tumour grade as confirmed by biopsy.
Results: The difference between the mean FD and GLCM parameters of the lesion vs the normal ROI were statistically significant (p,0.05); no significant difference was observed between the three grades of tumour for any of the parameters (p.0.05).
Conclusion: Texture analysis on CT images is a potential method in the characterization of oral cancers involving the buccal mucosa and deserves further investigation as a predictor of tumour aggression.
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