Multiple Sclerosis Disease Evolution Assessment in Brain MRI Lesions Based on Texture and Multi-Scale Amplitude Modulation-Frequency Modulation (AM-FM) Features

Abstract

Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T2W) images obtained from serial brain MRI scans at the baseline (Time0M) and the repeat (Time16-12M) examinations. The subjects were separated into two different groups based on their EDSS: (G1: 1≤EDSS2Y ≤ 3.5 (N=26) and G2: 3.5 < EDSS2Y ≤ 8.5 (N=12) and were monitored over ten years' time (Time10). After intensity normalization and image registration, texture and AM-FM features were extracted from all MS lesions at Time0M and Time6-12M. The extracted features were used to develop models that correlated with the disease progression in Time10Y. We found statistically significant differences for features extracted from the two different groups (G1 vs G2 at Time10Y) and these might be used to predict the development and or the severity of the MS disease. The best model for classifying G1 vs G2 subjects at Time10Y included information taken from the MS lesion images, texture features and AM-FM features extracted from those MS lesion images (with a correct classification score of %CC=94). The proposed methodology may contribute to additional factors for predicting the development and assessing the severity of the MS disease. However, a larger scale study is needed to establish the application in clinical practice and for computing additional features that may provide information for better and earlier differentiation between normal tissue and MS lesions.