Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome
AuthorLoizou, Christos P.
Pantzaris, Marios C.
Pattichis, Constantinos S.
SourceJournal of Neuroradiology
Google Scholar check
MetadataShow full item record
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) there was no significant difference for NAWM and the lesion texture features (for both 0 and 6-12. months) for subjects with no change in EDSS score versus subjects with increased EDSS score from 2 to 5. years. Conclusions: The findings of this study provide evidence that texture features of T2 MRI brain white matter lesions may have an additional potential role in the clinical evaluation of MRI images in MS and perhaps may provide some prognostic evidence in relation to future disability of patients. However, a larger scale study is needed to establish the application in clinical practice and for computing shape and texture features that may provide information for better and earlier differentiation between normal brain tissue and MS lesions. © 2014 Elsevier Masson SAS.
Showing items related by title, author, creator and subject.
Generalized least squares for assessing trends in cumulative meta-analysis with applications in genetic epidemiology Bagos, Pantelis G.; Nikolopoulos, Georgios K. (2009)Objective: Cumulative meta-analysis allows the evaluation of a study's contribution to the combined effect of the preceding research. It accrues evidence, gradually adding studies one at a time and provides updated estimates ...
Spectral density ratio based clustering methods for the binary segmentation of protein sequences: A comparative study Ioannou, Androulla; Fokianos, Konstantinos; Promponas, Vasilis J. (2010)We compare several spectral domain based clustering methods for partitioning protein sequence data. The main instrument for this exercise is the spectral density ratio model, which specifies that the logarithmic ratio of ...
Mixed-effects poisson regression models for meta-analysis of follow-up studies with constant or varying durations Bagos, Pantelis G.; Nikolopoulos, Georgios K. (2009)We present a framework for meta-analysis of follow-up studies with constant or varying duration using the binary nature of the data directly. We use a generalized linear mixed model framework with the Poisson likelihood ...