Background: Lesion load (LL), deep gray matter (DGM) and normal-appearing white matter (NAWM) susceptibility and morphometry may help in monitoring brain changes in multiple sclerosis (MS) patients. We aimed at evaluating the feasibility of a fully automated segmentation and the potential interrelation between these biomarkers and clinical disability.

Methods: Sixty-six patients with brain MRIs and clinical evaluations (Expanded Disability Status Scale [EDSS]) were retrospectively included. Automated prototypes were used for the segmentation and morphometry of brain regions (MorphoBox) and MS lesions (LeManPV). Susceptibility maps were estimated using standard post-processing (RESHARP and TVSB). Spearman's rho was computed to evaluate the interrelation between biomarkers and EDSS.

Results: We found (i) anticorrelations between the LL and right thalamus susceptibility (rho = -0.46, p < 0.001) and between the LL and NAWM susceptibility (rho = [-0.68 to -0.25], p ≤ 0.05); (ii) an anticorrelation between LL and DGM (rho = [-0.71 to -0.36], p < 0.04) and WM morphometry (rho = [-0.64 to -0.28], p ≤ 0.01); and (iii) a positive correlation between EDSS and LL (rho = [0.28 to 0.5], p ≤ 0.03) and anticorrelation between EDSS and NAWM susceptibility (rho = [-0.29 to -0.38], p < 0.014).

Conclusions: Fully automated brain morphometry and susceptibility monitoring is feasible in MS patients. The lesion load, thalamus and NAWM susceptibility values and trophicity are interrelated and correlate with disability.