Clinical 3-tesla FLAIR* MRI improves diagnostic accuracy in multiple sclerosis

OBJECTIVE

To evaluate clinical fluid-attenuated inversion recovery (FLAIR)* 3T magnetic resonance imaging (MRI), which is sensitive to perivenular inflammatory demyelinating lesions, in diagnosing multiple sclerosis (MS).

BACKGROUND

Central veins may be a distinguishing feature of MS lesions. FLAIR*, a combined contrast derived from clinical MRI scans, has not been studied as a clinical tool for diagnosing MS.

METHODS

Two experienced MS neurologists evaluated 87 scan pairs (T₂-FLAIR/FLAIR*), separately and side-by-side, from 68 MS cases, 8 healthy volunteers, and 11 individuals with other neurological diseases. Raters judged cases based on experience, published criteria, and a visual assessment of the "40% rule," whereby MS is favored if >40% of lesions demonstrate a central vein. Diagnostic accuracy was determined with area under the receiver operating characteristic curve (AUC), and inter-rater reliability was assessed with Cohen's kappa (κ).

RESULTS

Diagnostic accuracy was high: rater 1, AUC 0.94 (95% confidence interval: 0.89, 0.97) for T₂-FLAIR, 0.95 (0.92, 0.98) for FLAIR*; rater 2, 0.94 (0.90, 0.98) and 0.90 (0.85, 0.95). AUC improved when images were considered together: rater 1, 0.99 (0.98, 1.00); rater 2, 0.98 (0.96, 0.99). Inter-rater agreement was substantial for T₂-FLAIR (κ = 0.68) and FLAIR* (κ = 0.74), despite low agreement on the 40% rule (κ = 0.47) ([Formula: see text] in all cases).

CONCLUSIONS

Joint clinical evaluation of T₂-FLAIR and FLAIR* images modestly improves diagnostic accuracy for MS and does not require counting lesions with central veins.

Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging

BACKGROUND

New multiple sclerosis (MS) lesion activity on magnetic resonance imaging (MRI) can test immunomodulatory therapies in proof-of-concept trials. Comparably powerful endpoints to assess tissue protection or repair are lacking.

OBJECTIVE

The objective of this paper is to report sample-size calculations for assessment of new lesion recovery.

METHODS

In two sets of six active MS cases, new lesions were observed by monthly MRI for approximately 12 months. Averages and quartiles of normalized (proton density/T1/T2 weighted) and quantitative (T1/T2 and mean diffusivity maps for dataset 1, T2 and magnetization transfer ratio maps for dataset 2) measures were used to compare the lesion area before lesion appearance to afterward. A linear mixed-effects model incorporating lesion- and participant-specific random effects estimated average levels and variance components for sample-size calculations.

RESULTS

In both datasets, greatest statistical sensitivity was observed for the 25th percentile of normalized proton density-weighted signal. At 3T, using new lesions ⩾15 mm(3), as few as nine participants/arm may be required for a six-month placebo-controlled add-on trial postulating a therapeutic effect size of 20% and statistical power of 90%.

CONCLUSION

Lesion recovery is a powerful outcome measure for proof-of-concept clinical trials of tissue protection and repair in MS. The trial design requires active cases and is therefore best implemented near disease onset.

Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling

Brain atrophy, measured by MRI, has been proposed as a useful surrogate marker for disease progression in multiple sclerosis (MS). However, it is conventionally assumed that the accurate quantification of brain atrophy is made difficult, if not impossible, by changes in the parameters of the MRI acquisition, which are almost inevitable over the course of a longitudinal study since MRI technology changes rapidly. This state of affairs can negatively affect clinical trial design and limit the use of historical data. Here, we investigate whether we can coherently estimate brain atrophy rates in a heterogeneous MS sample via linear mixed-effects multivariable regression, incorporating three critical assumptions: (1) using age at time of scanning, rather than time since baseline, as the regressor of interest; (2) scanning individuals with a variety of techniques; and (3) introducing a simple additive correction for major differences in MRI protocol. We fit the model to several measures of brain volume as the outcome in two MS populations: 1123 scans from 195 cases acquired for over approximately 7 years in two natural history protocols (Cohort 1), and 1331 scans from 69 cases seen for over 11 years who were primarily treated with two specific MS disease-modifying therapies (Cohort 2). We compared the mixed-effects model with additive correction for MRI acquisition parameters to a model fit without this correction and performed sample-size calculations to provide an estimate of the number of participants in an MS clinical trial that might be required to see a therapeutic effect of treatment using the approach described here. The results show that without the additive correction for T1-weighted protocol parameters, atrophy was underestimated and subject-specific estimates were more narrowly distributed about the population mean. Ventricular CSF is the most consistently estimated brain volume, with a mean of 2.8%/year increase in Cohort 1 and 4.4%/year increase in Cohort 2. An interesting observation was that gray matter volume decreased and white matter volume remained essentially unchanged in both cohorts, suggesting that changes in ventricular CSF volume are a surrogate for changes in gray matter volume. In conclusion, the mixed-effects modeling framework presented here allows effective use of heterogeneously acquired and historical data in the study of brain atrophy in MS, potentially simplifying the design of future single- and multi-site clinical trials and natural history studies.

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We model brain atrophy in two heterogeneously acquired MS MRI datasets.

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Mixed-effects regression effectively adjusts for major MRI differences.

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Ventricular CSF tracks with gray matter but is more reliably estimated.

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The method would allow reasonable sample sizes in MS therapeutic trials.

The effect of daclizumab on brain atrophy in relapsing-remitting multiple sclerosis

Daclizumab is a monoclonal antibody that reduces inflammation in multiple sclerosis (MS). Through a retrospective analysis, our objective was to determine whether daclizumab treatment reduces the rate of brain structure atrophy in comparison to a mixture of other disease-modifying therapies (mainly different interferon β preparations). We analyzed MRI examinations (1332 scans from 70 MS cases) obtained between 2000 and 2011 in a single center and processed with an automated brain segmentation method. We used mixed-effects multivariable linear regression models to determine whether a median of 4.3 years of daclizumab therapy in 26 patients altered rates of brain-volume change, controlling for variations in MRI protocol. The control group consisted of 44 patients not treated with daclizumab. We found that supratentorial brain volume declined by 5.17 ml per year (95% confidence limits: 3.58-6.77) off daclizumab therapy. On daclizumab, the annual rate of volume loss decreased to 3.72 ml (p=0.01). The rate of ventricular enlargement decreased from 1.26 to 0.42 ml per year (p<0.001). Focused analysis suggests that reduction in gray matter atrophy rate most likely underlies these results. In summary, in this retrospective analysis, daclizumab therapy substantially decreased the rate of brain atrophy in relapsing-remitting MS in comparison to other disease-modifying therapies, predominantly interferon β.

Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI

BACKGROUND AND PURPOSE

Detecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls stable lesions, leaving only new lesion activity. We propose SuBLIME, an automated method for segmenting incident lesion voxels.

MATERIALS AND METHODS

We used logistic regression models incorporating multiple MR imaging sequences and subtraction images from consecutive longitudinal studies to estimate voxel-level probabilities of lesion incidence. We used T1-weighted, T2-weighted, FLAIR, and PD volumes from a total of 110 MR imaging studies from 10 subjects.

RESULTS

To assess the performance of the model, we assigned 5 subjects to a training set and the remaining 5 to a validation set. With SuBLIME, lesion incidence is detected and delineated in the validation set with an AUC of 99% (95% CI [97%, 100%]) at the voxel level.

CONCLUSIONS

This fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.

FLAIR*: a combined MR contrast technique for visualizing white matter lesions and parenchymal veins

PURPOSE

To evaluate a magnetic resonance (MR) imaging contrast technique, called FLAIR*, that combines the advantages of T2-weighted fluid-attenuated inversion recovery (FLAIR) contrast and T2*-weighted contrast on a single image for assessment of white matter (WM) diseases such as multiple sclerosis (MS).

MATERIALS AND METHODS

This prospective pilot study was HIPAA compliant and institutional review board approved. Ten patients with clinically definite MS (eight men, two women; mean age, 41 years) provided informed consent and underwent 3.0-T MR imaging. Images from a T2-weighted FLAIR sequence were combined with images from a T2*-weighted segmented echo-planar imaging sequence performed during contrast material injection, yielding high-isotropic-resolution (0.55 × 0.55 × 0.55 mm(3)) FLAIR* images. Qualitative assessment was performed for image quality, lesion conspicuity, and vein conspicuity. Contrast-to-noise ratio (CNR) was calculated to compare normal-appearing WM (NAWM) with cerebrospinal fluid, lesions, and veins. To evaluate the differences in CNR among imaging modalities, a bootstrap procedure clustered on subjects was used, together with paired t tests.

RESULTS

High-quality FLAIR* images of the brain were produced at 3.0 T, yielding conspicuous lesions and veins. Lesion-to-NAWM and NAWM-to-vein CNR values were significantly higher for FLAIR* images than for T2-weighted FLAIR images (P < .0001). Findings on FLAIR* images included intralesional veins for lesions located throughout the brain and a hypointense rim around some WM lesions.

CONCLUSION

High-isotropic-resolution FLAIR* images obtained at 3.0 T yield high contrast for WM lesions and parenchymal veins, making it well suited to investigate the relationship between WM abnormalities and veins in a clinical setting.

Evolution of the blood-brain barrier in newly forming multiple sclerosis lesions

OBJECTIVE

Multiple sclerosis (MS) lesions develop around small, inflamed veins. New lesions enhance with gadolinium on magnetic resonance imaging (MRI), reflecting disruption of the blood-brain barrier (BBB). Single time point results from pathology and standard MRI cannot capture the spatiotemporal expansion of lesions. We investigated the development and expansion of new MS lesions, focusing on the dynamics of BBB permeability.

METHODS

We performed dynamic contrast-enhanced (DCE) MRI in relapsing-remitting MS. We obtained data over 65 minutes, during and after gadolinium injection. We labeled spatiotemporal enhancement dynamics as centrifugal when initially central enhancement expanded outward and centripetal when initially peripheral enhancement gradually filled the center.

RESULTS

We detected 34 enhancing lesions in 200 DCE-MRI scans. In 65%, enhancement first appeared as a closed ring; in 18%, as a nodule; and in 18%, as an open ring. Lesions with initially nodular enhancement were smaller than those initially enhancing as rings (p < 0.0001). All initially nodular lesions enhanced centrifugally, whereas initially ringlike lesions enhanced centripetally, becoming nodular if small (82%) or nearly nodular if larger (18%). Open-ring lesions were periventricular or juxtacortical and enhanced centripetally. Centrifugally enhancing lesions evolved into centripetally enhancing lesions over several days.

INTERPRETATION

The rapid change of enhancement dynamics from centrifugal to centripetal reflects the outward growth of MS lesions around their central vein and suggests that factors mediating lesion growth and tissue repair derive from different locations at different times. We propose a model of new lesion growth that unites our imaging observations with existing pathology data.