Quantification of Thalamic Atrophy in MS: From the Multicenter Italian Neuroimaging Network Initiative Data Set to Clinical Application

BACKGROUND AND PURPOSE: Thalamic atrophy occurs from the earliest phases of MS; however, this measure is not included in clinical practice. Our purpose was to obtain a reliable segmentation of the thalamus in MS by comparing existing automatic methods cross-sectionally and longitudinally.MATERIALS AND METHODS: MR images of 141 patients with relapsing-remitting MS (mean age, 38 years; range, 19-58 years; 95 women) and 69 healthy controls (mean age, 36 years; range, 22-69 years; 47 women) were retrieved from the Italian Neuroimaging Network Initiative repository: T1WI, T2WI, and DWI at baseline and after 1 year (136 patients, 31 healthy controls). Three segmentation software programs (FSL-FIRST, FSL-MIST, FreeSurfer) were compared. At baseline, agreement among pipelines, correlations with age, disease duration, clinical score, and T2-hyperintense lesion volume were evaluated. Effect sizes in differentiating patients and controls were assessed cross-sectionally and longitudinally. Variability of longitudinal changes in controls and sample sizes were assessed. False discovery rate-adjusted P <.05 was considered significant.RESULTS: At baseline, FSL-FIRST and FSL-MIST showed the highest agreement in the results of thalamic volume (R = 0.87, P <.001), with the highest effect size for FSL-MIST (Cohen d = 1.11); correlations with demographic and clinical variables were comparable for all software. Longitudinally, FSL-MIST showed the lowest variability in estimating thalamic volume changes for healthy controls (SD= 1.07%), the highest effect size (Cohen d = 0.44), and the smallest sample size at 80% power level (15 subjects per group).CONCLUSIONS: Multimodal segmentation by FSL-MIST increased the robustness of the results with better capability to detect small variations in thalamic volumes.

Automated summary

The use of FSL-MIST multimodal segmentation method improves the robustness of results and enables better detection of small variations in thalamic volumes.