Diffusivity parameters of diffusion tensor imaging and apparent diffusion coefficient as imaging markers for predicting the treatment response of patients with trigeminal neuralgia

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OBJECTIVE

Trigeminal neuralgia (TN) is facial pain that is usually caused by neurovascular compression syndrome and is characterized by suddenly intense and paroxysmal pain. Radiofrequency lesioning (RFL) is one of the major treatments for TN, but the treatment response for RFL is sometimes inconsistent, and the recurrence of TN is not uncommon. This study aimed to estimate the outcome predictors of TN treated with RFL by using the parameters of diffusion tensor imaging (DTI).

METHODS

Fifty-one patients with TN who were treated with RFL were enrolled in the study. MRI was performed in all patients within 1 week before surgery. The visual analog scale was used to evaluate symptom severity at three time points: before, 1 week after, and 3 months after RFL. The involved cisternal segment of the trigeminal nerves was manually selected, and the histograms of each of the diffusivity metrics—including the apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD)—were measured. The differences in the means, as well as the kurtosis and skewness of each of the diffusivity metrics between the nonrecurrent and recurrent groups, were then analyzed using the Mann-Whitney U-test.

RESULTS

There were significantly lower kurtosis values (a broader peak of the distributional curves) for both FA and ADC in the recurrent group (p = 0.0004 and 0.015, respectively), compared to the nonrecurrent group. The kurtoses of AD and RD, as well as the mean and skewness of all other diffusivity metrics, did not show significant differences between the two groups.

CONCLUSIONS

The pretreatment diffusivity metrics of DTI and ADC may be feasible imaging biomarkers for predicting the outcome of TN after RFL. A clarification of the kurtosis value of FA and ADC is helpful for determining the prognosis of patients after RFL.

ABBREVIATIONS AD = axial diffusivity; ADC = apparent diffusion coefficient; DTI = diffusion tensor imaging; FA = fractional anisotropy; FOV = field of view; GKS = Gamma Knife surgery; ICC = intraclass correlation coefficient; MPRAGE = magnetization-prepared rapid acquisition gradient echo; NVCS = neurovascular compression syndrome; RD = radial diffusivity; REZ = root entry zone; RFL = radiofrequency lesioning; ROI = region of interest; TN = trigeminal neuralgia; VAS = visual analog scale.

Article Information

Correspondence Yuan-Hsiung Tsai: Chang Gung Memorial Hospital, Chiayi, Taiwan. russell.tsai@gmail.com.

INCLUDE WHEN CITING Published online May 17, 2019; DOI: 10.3171/2019.2.JNS183008.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.

Headings

Figures

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    Flowchart of patient selection.

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    Example of diffusivity metrics measurement in a 55-year-old patient with TN. All imaging voxels covering the cisternal segment of the affected trigeminal nerve (yellow boxes) were manually selected on the diffusion tensor images by an experienced neuroradiologist, and multiple sections of the ROI were considered to avoid partial volume effects (A). In addition, diffusion tensor images (B) and 3D MPRAGE anatomical images (C) in the axial plane were simultaneously displayed for cross-reference.

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    Illustration of different types of kurtosis curves. Kurtosis is a dimensionless statistical metric that quantifies the deviation from Gaussianity of an arbitrary distribution and measures the data that are more or less around the mean or have a larger variance. The kurtosis of a normal distribution is equal to 3, and we usually define kurtosis in terms of “excess kurtosis,” taking 0 as the baseline (black line). Having many data points near the mean will result in a thin bell curve with a high peak in the middle, which we call positive kurtosis or “leptokurtic” (blue line). In contrast, a distribution that contains many values that are farther away from the mean will have a flat frequency distribution curve with a broad peak, which we classify as negative kurtosis or “platykurtic” (green line). For example, the recurrent group investigated in our study deviated significantly from the normal distribution because the data were less concentrated around the mean and had a large variance.

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    Schematic illustration of water diffusivity in normal and abnormal myelinated nerve fibers. Affected nerves of vascular compression express demyelination, abnormal remyelinization, and variable thickness of nerve sheaths, and may result in heterogeneous microstructural integrity and altered movement of water. The water diffusivity (red arrows) will change the directionality, which initially presents in a consistent direction parallel to the nerve sheaths, and then turns into chaotic diffusion.

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