Comparing longitudinal brain atrophy measurement techniques in a real-world multiple sclerosis clinical practice cohort: towards clinical integration?

BACKGROUND: 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. METHODS: 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. RESULTS: 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. DISCUSSION: 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.

The evolution of "No Evidence of Disease Activity" in multiple sclerosis.

The availability of effective therapies for patients with relapsing-remitting multiple sclerosis (RRMS) has prompted a re-evaluation of the most appropriate way to measure treatment response, both in clinical trials and clinical practice. Traditional parameters of treatment efficacy such as annualized relapse rate, magnetic resonance imaging (MRI) activity, and disability progression have an important place, but their relative merit is uncertain, and the role of other factors such as brain atrophy is still under study. More recently, composite measures such as "no evidence of disease activity" (NEDA) have emerged as new potential treatment targets, but NEDA itself has variable definitions, is not well validated, and may be hard to implement as a treatment goal in a clinical setting. We describe the development of NEDA as an outcome measure in MS, discuss definitions including NEDA-3 and NEDA-4, and review the strengths and limitations of NEDA, indicating where further research is needed.

Automated brain volumetrics in multiple sclerosis: a step closer to clinical application.

BACKGROUND: Whole brain volume (WBV) estimates in patients with multiple sclerosis (MS) correlate more robustly with clinical disability than traditional, lesion-based metrics. Numerous algorithms to measure WBV have been developed over the past two decades. We compare Structural Image Evaluation using Normalisation of Atrophy-Cross-sectional (SIENAX) to NeuroQuant and MSmetrix, for assessment of cross-sectional WBV in patients with MS. METHODS: MRIs from 61 patients with relapsing-remitting MS and 2 patients with clinically isolated syndrome were analysed. WBV measurements were calculated using SIENAX, NeuroQuant and MSmetrix. Statistical agreement between the methods was evaluated using linear regression and Bland-Altman plots. Precision and accuracy of WBV measurement was calculated for (1) NeuroQuant versus SIENAX and (2) MSmetrix versus SIENAX. RESULTS: Precision (Pearson's r) of WBV estimation for NeuroQuant and MSmetrix versus SIENAX was 0.983 and 0.992, respectively. Accuracy (Cb) was 0.871 and 0.994, respectively. NeuroQuant and MSmetrix showed a 5.5% and 1.0% volume difference compared with SIENAX, respectively, that was consistent across low and high values. CONCLUSIONS: In the analysed population, NeuroQuant and MSmetrix both quantified cross-sectional WBV with comparable statistical agreement to SIENAX, a well-validated cross-sectional tool that has been used extensively in MS clinical studies.