Differentiating Neuromyelitis Optica–Related and Multiple Sclerosis–Related Acute Optic Neuritis Using Conventional Magnetic Resonance Imaging Combined With Readout-Segmented Echo-Planar Diffusion-Weighted Imaging

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Abstract

Purpose

In clinical practice, acute optic neuritis (ON) associated with the development of neuromyelitis optica (NMO) after the first attack is often indistinguishable from that associated with multiple sclerosis (MS). We aimed to determine the optimal combination of features derived from conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging using readout-segmented echo-planar imaging (RESOLVE-DWI) for the differentiation of these conditions.

Materials and Methods

Orbital conventional MRI and RESOLVE-DWI were performed using a 3.0-T scanner on 54 patients with acute ON (26 NMO-related and 28 MS-related). The features detected by conventional MRI (including laterality, the enhancement pattern, and the extent and position of involvement) and the apparent diffusion coefficient (ADC) measurements were retrospectively compared between the NMO-related and MS-related groups. A multivariate logistic regression analysis was used to identify the most significant variables, and receiver operating characteristic curve analyses were performed to determine the ability of a combined diagnostic model based on the qualitative and quantitative characteristics identified in this study to differentiate the 2 conditions.

Results

The multivariate logistic regression analyses indicated that the presence of chiasm involvement and lower ADC values were significantly associated with NMO-related acute ON compared with MS-related acute ON (P = 0.037 and 0.008, respectively). The diagnostic criterion of chiasm involvement or “ADC < 791 × 10−6 mm2/s and chiasm involvement” had the highest specificity (96.9%), and “ADC < 791 × 10−6 mm2/s or chiasm involvement” showed the optimal sensitivity (77.8%) for differentiating NMO-related from MS-related acute ON.

Conclusions

Conventional MRI RESOLVE-DWI is helpful for differentiating NMO-related acute ON from MS-related acute ON. The combination of the ADC value chiasm involvement appears to be effective for discriminating these 2 types of acute ON.

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