This study explores the efficacy of texture analysis by using preoperative multi-slice spiral computed tomography (MSCT) to non-invasively determine the grade of cellular differentiation in head and neck squamous cell carcinoma (HNSCC). In a retrospective study, MSCT scans of patients with HNSCC were analyzed and classified based on its histological grade as moderately differentiated, well-differentiated, or poorly differentiated. The location of the tumor was categorized as either in the bone or in soft tissues. Segmentation of the lesion areas was conducted, followed by texture analysis. Eleven GLCM parameters across five different distances were calculated. Median values and correlations of texture parameters were examined in relation to tumor differentiation grade by using Spearman’s correlation coefficient and Kruskal–Wallis and Dunn tests. Forty-six patients were included, predominantly female (87%), with a mean age of 66.7 years. Texture analysis revealed significant parameter correlations with histopathological grades of tumor differentiation. The study identified no significant age correlation with tumor differentiation, which underscores the potential of texture analysis as an age-independent biomarker. The strong correlations between texture parameters and histopathological grades support the integration of this technique into the clinical decision-making process.

Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA,p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA,p < 0.05) dependence with age; however, the R²indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

Several studies have shown structural brain changes resulting from physical exercise. White and gray matter volumes have been reported to increase as a result of physical training in healthy individuals, and a reduction in brain structure atrophy has also been associated with physical activity. However, the assessment of magnetic resonance imaging (MRI) texture changes related to physical exercise has not been investigated. This study aimed to evaluate texture-based structural brain changes in initially sedentary young (< 30 years) participants who completed a moderate-intensity running training program. Structural MR images were obtained before and after the program. Texture features were extracted using the Gray-Level Co-occurrence Matrix (GLCM) and compared between time points to verify changes due to aerobic physical activity. The correlation parameter significantly increased for the left globus pallidus after the intervention. Non-significant trends toward increased correlation were also observed for the right globus pallidus, left hippocampus, right cerebellum, and left precentral gyrus, whereas a decreasing trend was observed for the right inferior frontal gyrus. These findings suggest that texture analysis may be a useful approach for detecting subtle changes in brain MRI associated with cardiovascular exercise in young adults and support further investigation of exercise-related neuroplastic adaptations.

It is well known that the brain changes as we age. Several magnetic resonance imaging (MRI)-based studies have demonstrated structural changes in the brain, such as cortical thickness and volume. Conversely, few studies have investigated structural changes derived from MRI texture analysis. Texture analysis reflects underlying tissue characteristics, thus allowing the quantification of subtle structural variations in brain tissues that may not be apparent through standard image inspection. The aim of this study was to observe and compare brain texture in MRI images between healthy young and elderly individuals. To this end, MRI images from two groups, one of young and one of elderly individuals, were analyzed using the texture analysis technique with a second-order statistical approach, namely the gray-level co-occurrence matrix (GLCM). GLCMs computed in three dimensions were fitted to five intervoxel distances, and contrast and entropy parameters were extracted and compared between the groups. Statistically significant differences were found between the groups for both contrast and entropy parameters, for all intervoxel distances, suggesting that brain structure changes occur during aging. This difference occurred between brain areas related to memory, reasoning, numerical processing, and language. This study analyzed two different groups of individuals; in the future, it would be interesting to conduct longitudinal studies evaluating the same individuals over time to corroborate these findings.