Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome

• Published in: Journal of neuroradiology 2015-04, Vol.42 (2), p.99-114
• Main Authors: Loizou, C.P, Petroudi, S, Seimenis, I, Pantziaris, M, Pattichis, C.S
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Demyelinating Diseases - pathology; Diffusion Tensor Imaging - methods; EDSS; Female; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Male; MRI; Multiple sclerosis; Multiple Sclerosis - pathology; Pattern Recognition, Automated - methods; Radiology; Reproducibility of Results; Sensitivity and Specificity; Shape features; Texture analysis; White Matter - pathology
• ISSN: 0150-9861
• DOI: 10.1016/j.neurad.2014.05.006

Summary Introduction This study investigates the application of texture analysis methods on brain T2-white matter lesions detected with magnetic resonance imaging (MRI) for the prognosis of future disability in subjects diagnosed with clinical isolated syndrome (CIS) of multiple sclerosis (MS). Methods Brain lesions and normal appearing white matter (NAWM) from 38 symptomatic untreated subjects diagnosed with CIS as well as normal white matter (NWM) from 20 healthy volunteers, were manually segmented, by an experienced MS neurologist, on transverse T2-weighted images obtained from serial brain MR imaging scans (0 and 6–12 months). Additional clinical information in the form of the Expanded Disability Status Scale (EDSS), a scale from 0 to 10, which provides a way of quantifying disability in MS and monitoring the changes over time in the level of disability, were also provided. Shape and most importantly different texture features including GLCM and laws were then extracted for all above regions, after image intensity normalization. Results The findings showed that: (i) there were significant differences for the texture futures extracted between the NAWM and lesions at 0 month and between NAWM and lesions at 6–12 months. However, no significant differences were found for all texture features extracted when comparing lesions temporally at 0 and 6–12 months with the exception of contrast (gray level difference statistics-GLDS) and difference entropy (spatial gray level dependence matrix-SGLDM); (ii) significant differences were found between NWM and NAWM for most of the texture features investigated in this study; (iii) there were significant differences found for the lesion texture features at 0 month for those with EDSS ≤ 2 versus those with EDSS > 2 (mean, median, inverse difference moment and sum average) and for the lesion texture features at 6–12 months with EDSS > 2 and EDSS ≤ 2 for the texture features (mean, median, entropy and sum average). It should be noted that whilst there were no differences in entropy at time 0 between the two groups, significant change was observed at 6–12 months, relating the corresponding features to the follow-up and disability (EDSS) progression. For the NAWM, significant differences were found between 0 month and 6–12 months with EDSS ≤ 2 (contrast, inverse difference moment), for 6–12 months for EDSS > 2 and 0 month with EDSS > 2 (difference entropy) and for 6–12 months for EDSS > 2 and EDSS ≤ 2 (sum average); (iv) the