Effectiveness and baseline factors associated to fingolimod response in a real-world study on multiple sclerosis patients.

BACKGROUND: Treatment choice in multiple sclerosis (MS) is crucial for optimizing risk-benefit profile. OBJECTIVE: To assess fingolimod (FTY) effectiveness and identify baseline features associated to disease activity in a large Italian cohort of Relapsing-Remitting (RR) MS patients. METHODS: Three-hundred sixty-seven RRMS patients starting FTY treatment at San Raffaele Hospital (Milan-Italy) underwent clinical and MRI evaluations for 2 years. Treatment response was assessed considering the proportion of patients with no evidence of disease activity (NEDA) and recording the time to first relapse. Primary analyses were performed stratifying for Natalizumab (NTZ) treatment in the year before (NO_NTZ vs NTZ group), to account for post-NTZ reactivation. RESULTS: Almost half of patients were NEDA after 2 years, 53.4% in the NO_NTZ group and 36.2% in the NTZ group. Despite an opposite trend during the first 6-12 months, at 2-year follow-up the two groups were comparable for relapses and number of new/enlarging T2 and Gd-enhancing lesions. Baseline parameters of higher disease activity (ARR, Gd enhancing lesions and age at onset) were associated with increased likelihood of failing NEDA criteria or with shorter time to relapse (p < 0.05). CONCLUSIONS: Our data strengthen FTY effectiveness in everyday clinical practice, even in patients switching from NTZ treatment. Baseline parameters of inflammatory activity are the most important prognostic factors for mid-term disease reactivation also during second-line treatment with FTY, providing hints on how to select therapies towards a more personalized management.

A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context.

BACKGROUND AND PURPOSE: The automatic segmentation of MS lesions could reduce time required for image processing together with inter- and intraoperator variability for research and clinical trials. A multicenter validation of a proposed semiautomatic method for hyperintense MS lesion segmentation on dual-echo MR imaging is presented. MATERIALS AND METHODS: The classification technique used is based on a region-growing approach starting from manual lesion identification by an expert observer with a final segmentation-refinement step. The method was validated in a cohort of 52 patients with relapsing-remitting MS, with dual-echo images acquired in 6 different European centers. RESULTS: We found a mathematic expression that made the optimization of the method independent of the need for a training dataset. The automatic segmentation was in good agreement with the manual segmentation (dice similarity coefficient = 0.62 and root mean square error = 2 mL). Assessment of the segmentation errors showed no significant differences in algorithm performance between the different MR scanner manufacturers (P > .05). CONCLUSIONS: The method proved to be robust, and no center-specific training of the algorithm was required, offering the possibility for application in a clinical setting. Adoption of the method should lead to improved reliability and less operator time required for image analysis in research and clinical trials in MS.