The Role of High-Frequency MRI Monitoring in the Detection of Brain Atrophy in Multiple Sclerosis

Interpretation of Brain Volume Increase in Multiple Sclerosis

BACKGROUND AND PURPOSE

A high variability of brain MRI volume change measurement renders challenging its interpretation in multiple sclerosis (MS). Occurrence and clinical relevance of observed apparent brain volume increase (BVI) in MS patients have not been investigated yet. The objective was to quantify the prevalence and factors associated with BVI.

METHODS

We examined 366 MS patients (2,317 scans) and 44 controls (132 scans). Volumetric analysis of brain volume changes was performed by SIENA and ScanView. BVI was defined as brain volume change >0%. We compared characteristics of patients with and without BVI.

RESULTS

BVI was found in 26.3% (from 1,951) longitudinal scans (SIENA). If BVI occurred, a probability that BVI will be repeated consecutively more than or equal to two times was 15.9%. The repeated BVI was associated with clinical disease activity in 50% of cases. BVI was associated with shorter time and lower T2 lesion volume increase between two MRI scans, and higher normalized brain volume (all P < .0001). A proportion of scans with BVI was higher when analyzed by ScanView (35.3%) and in controls (36.4% by SIENA).

CONCLUSIONS

BVI occurs in a great proportion of MR scans over short-term follow-up and is not associated with disease stabilization. Although BVI can be caused by several factors, the results indicate that measurement error may contribute to BVI in the majority of cases. Clinicians should be aware of the frequent occurrence of apparent BVI, interpret brain volume changes in MS patients with great caution, and use methods with precise quantification of brain volume changes.

Adding brain volume measures into response criteria in multiple sclerosis: the Río-4 score

PURPOSE

Brain volume changes (BVC) on therapy in MS are being considered as predictor for treatment response at an individual level. We ought to assess whether adding BVC as a factor to monitor interferon-beta response improves the predictive ability of the (no) evidence of disease activity (EDA-3) and Río score (RS-3) criteria for confirmed disability progression in a historical cohort.

METHODS

One hundred one patients from an observational cohort treated with interferon-beta were assessed for different cutoff points of BVC (ranged 0.2-1.2%), presence of active lesions (≥ 1 for EDA/≥ 3 for RS), relapses, and 6-month confirmed disability progression (CDP), measured by the Expanded Disability Status Scale, after 1 year. Sensitivity, specificity, and positive and negative predictive values for predicting confirmed disability progression at 4 years in original EDA (EDA-3) and RS (RS-3) as well as EDA and RS including BVC (EDA-4 and RS-4) were compared.

RESULTS

Adding BVC to EDA slightly increased sensitivity, but not specificity or predictive values, nor the OR for predicting CDP; only EDA-3 showed a trend for predicting CDP (OR 3.701, p = 0.050). Adding BVC to RS-3 (defined as ≥ 2 criteria) helped to improve sensitivity and negative predictive value, and increased OR for predicting CDP using a cutoff of ≤ - 0.86% (RS-3 OR 23.528, p < 0.001; RS-4 for all cutoffs ranged from 15.06 to 32, p < 0.001). RS-4 showed areas under the curve larger than RS-3 for prediction of disability at 4 years.

CONCLUSION

Addition of BVC to RS improves its prediction of response to interferon-beta.

Aging and Brain Atrophy in Multiple Sclerosis

BACKGROUND AND PURPOSE

Brain atrophy accelerates at the age of 60 in healthy individuals (HI) and at disease onset in multiple sclerosis (MS) patients. Whether there is an exacerbating effect of aging superimposed on MS-related brain atrophy is unknown. We estimated the aging effect on lateral ventricular volume (LVV) and whole brain volume (WBV) changes in MS patients.

METHODS

1,982 MS patients (mean follow-up: 4.8 years) and 351 HI (mean follow-up: of 3.1 years), aged from 20 to 79 years old (yo), were collected retrospectively. Percent LVV change (PLVVC) and percent brain volume change (PBVC) on 1.5T and 3T MRI scanners (median of 3.9 scans per subject) were calculated. These were determined between all-time points and subjects were divided in six-decade age groups. MRI differences between age groups were calculated using analysis of covariance (ANCOVA).

RESULTS

Compared to HI, at first MRI, MS patients had significantly increased LVV in the age groups: 30-39 yo, 40-49 yo, 50-59 yo, 60-69 yo (all P < .0001), and 70-79 yo (P = .029), and decreased WBV in the age groups: 20-29 yo (P = .024), 30-39 yo (P = .031), 40-49 yo, and 50-59 yo (all P < .0001). Annualized PLVVC was significantly different between the age groups 20-59 and 60-79 yo in MS patients (P = .005) and HI (P < .0001), as was for PBVC in MS patients (P = .001), but not for HI (P = .521). There was a significant aging interaction effect in the annualized PLVVC (P = .001) between HI and MS patients, which was not observed for the annualized PBVC (P = .380).

CONCLUSIONS

Development of brain atrophy manifests progressively in MS patients, and occurs with a different pattern, as compared to aging HI. PLVVC increased across age in HI as compared to MS, while PBVC decreased across ages in both HI and MS.

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.