title: Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions.
abstract: Brain blood barrier breakdown as assessed by contrast-enhanced (CE) T1-weighted MR imaging is currently the standard radiological marker of inflammatory activity in multiple sclerosis (MS) patients. Our objective was to evaluate the performance of an alternative model assessing the inflammatory activity of MS lesions by texture analysis of T2-weighted MR images. Twenty-one patients with definite MS were examined on the same 3.0T MR system by T2-weighted, FLAIR, diffusion-weighted and CE-T1 sequences. Lesions and mirrored contralateral areas within the normal appearing white matter (NAWM) were characterized by texture parameters computed from the gray level co-occurrence and run length matrices, and by the apparent diffusion coefficient (ADC). Statistical differences between MS lesions and NAWM were analyzed. ROC analysis and leave-one-out cross-validation were performed to evaluate the performance of individual parameters, and multi-parametric models using linear discriminant analysis (LDA), partial least squares (PLS) and logistic regression (LR) in the identification of CE lesions. ADC and all but one texture parameter were significantly different within white matter lesions compared to within NAWM (p < 0.0167). Using LDA, an 8-texture parameter model identified CE lesions with a sensitivity Se = 70% and a specificity Sp = 76%. Using LR, a 10-texture parameter model performed better with Se = 86% / Sp = 84%. Using PLS, a 6-texture parameter model achieved the highest accuracy with Se = 88% / Sp = 81%. Texture parameter from T2-weighted images can assess brain inflammatory activity with sufficient accuracy to be considered as a potential alternative to enhancement on CE T1-weighted images.
authors.affiliation: IREC (Institute of Experimental and Clinical Research), Université Catholique de Louvain, Brussels, Belgium.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Michoux
authors.affiliation: Department of Statistics, Université Catholique de Louvain, Brussels, Belgium.
authors.forename: Alain
authors.initials: A
authors.lastname: Guillet
authors.affiliation: Department of Radiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium.
authors.forename: Denis
authors.initials: D
authors.lastname: Rommel
authors.affiliation: Department of Radiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium.
authors.forename: Giosué
authors.initials: G
authors.lastname: Mazzamuto
authors.affiliation: Department of Neurology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium.
authors.forename: Christian
authors.initials: C
authors.lastname: Sindic
authors.affiliation: Department of Radiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium.
authors.forename: Thierry
authors.initials: T
authors.lastname: Duprez
mesh_terms: D001812:Blood-Brain Barrier; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007249:Inflammation / Q000000981:diagnostic imaging* / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000000981:diagnostic imaging / Q000473:pathology*; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods