Comparing longitudinal brain atrophy measurement techniques in a real-world multiple sclerosis clinical practice...

Whole brain atrophy (WBA) estimates in multiple sclerosis (MS) correlate more robustly with clinical disability than traditional, lesion-based metrics. We compare Structural Image Evaluation using Normalisation of Atrophy (SIENA) with the icobrain longitudinal pipeline (icobrain long), for assessment of longitudinal WBA in MS patients.

Magnetic resonance imaging (MRI) scan pairs [1.05 (±0.15) year separation] from 102 MS patients were acquired on the same 3T scanner. Three-dimensional (3D) T1-weighted and two-dimensional (2D)/3D fluid-attenuated inversion-recovery sequences were analysed. Percentage brain volume change (PBVC) measurements were calculated using SIENA and icobrain long. Statistical correlation, agreement and consistency between methods was evaluated; MRI brain volumetric and clinical data were compared. The proportion of the cohort with annualized brain volume loss (aBVL) rates ⩾ 0.4%, ⩾0.8% and ⩾0.94% were calculated. No evidence of disease activity (NEDA) 3 and NEDA 4 were also determined.

Mean annualized PBVC was -0.59 (±0.65)% and -0.64 (±0.73)% as measured by icobrain long and SIENA. icobrain long and SIENA-measured annualized PBVC correlated strongly, r = 0.805 (p < 0.001), and the agreement [intraclass correlation coefficient (ICC) 0.800] and consistency (ICC 0.801) were excellent. Weak correlations were found between MRI metrics and Expanded Disability Status Scale scores. Over half the cohort had aBVL ⩾ 0.4%, approximately a third ⩾0.8%, and aBVL was ⩾0.94% in 28.43% and 23.53% using SIENA and icobrain long, respectively. NEDA 3 was achieved in 35.29%, and NEDA 4 in 15.69% and 16.67% of the cohort, using SIENA and icobrain long to derive PBVC, respectively.

icobrain long quantified longitudinal WBA with a strong level of statistical agreement and consistency compared to SIENA in this real-world MS population. Utility of WBA measures in individuals remains challenging, but show promise as biomarkers of neurodegeneration in MS clinical practice. Optimization of MRI analysis algorithms/techniques are needed to allow reliable use in individuals. Increased levels of automation will enable more rapid clinical translation.