Imaging heterogeneity in gliomas using texture analysis

Abstract

Aim: To preliminary assess whether computed tomography texture analysis (CTTA) has the potential to quantify tumour heterogeneity and thereby differentiate high-grade from low-grade gliomas. Methods: The study comprised 44 patients with brain tumours. Histology identified 33 as high-grade and 11 as low-grade gliomas. Contrast-enhanced CT images were acquired prior to brain biopsy. CTTA was carried out using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity within the CT image (with or without filtration) was quantified using the following statistical parameters: entropy (indicates irregularity), uniformity (distribution of grey levels), total pixels (TP; indicates size), mean grey-level value and proportion of only those pixels with positive values (MPP and PPP), standard-deviation (SD), kurtosis (indicates peakedness) and skewness (indicates asymmetry). Texture parameters from high and low grade were compared with each other. Results: High grade demonstrated increased heterogeneity as measured from CTTA (entropy ↑, uniformity ↓, TP ↑, PPP ↓, SD ↑, skewness↑). Significant differences between tumour heterogeneity for high and low grade was observed for medium to coarse textures, with the highest being coarse textures; quantified as entropy (P = 0.0015), uniformity (P = 0.0026). Coarse texture values for entropy of >5.2(sensitivity 76%, specificity 82%), ≤0.025(sensitivity 64%, specificity 91%) for uniformity, was suggestive of high-grade glioma. TP (sensitivity 70%, specificity 73%) was higher in high grade than low grade, however less significant than texture. Conclusion: CTTA (e.g. TexRAD) has the potential to quantify tumour heterogeneity more so than size and thereby differentiate between high- and low-grade gliomas.