Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders by multiparametric quantitative MRI using convolutional neural network. (May 2021)

• Record Type: Journal Article
• Authors: Hagiwara, Akifumi; Otsuka, Yujiro; Andica, Christina; Kato, Shimpei; Yokoyama, Kazumasa; Hori, Masaaki; Fujita, Shohei; Kamagata, Koji; Hattori, Nobutaka; Aoki, Shigeki
• Is Part Of: Journal of clinical neuroscience. Volume 87(2021)
• Page Start: 55
• Page End: 58
• Publication Date: 2021-05
• Subjects: Deep learning -- Multiple sclerosis -- Magnetic resonance imaging -- Neuromyelitis optica spectrum disorder -- Multiparametric quantitative imaging; Brain -- Surgery -- Periodicals; Neurosciences -- Periodicals; Nervous system -- Surgery -- Periodicals; Brain -- surgery -- Periodicals; Neurosurgical Procedures -- Periodicals; Neurosciences -- Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.jocn.2021.02.018
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders is crucial. A convolutional neural network was developed to differentiate between these two diseases. The network is based on multiparametric quantitative MRI. The area under the receiver operating characteristic curve of the model was 0.859. Abstract: Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic resonance imaging data. Thirty-five patients with relapsing-remitting multiple sclerosis and eighteen age-, sex-, disease duration-, and Expanded Disease Status Scale-matched patients with anti-aquaporin-4 antibody-positive neuromyelitis optica spectrum disorders were included in this study. All patients were scanned on a 3-T scanner using a multi-dynamic multi-echo sequence that simultaneously measures R1 and R2 relaxation rates and proton density. R1, R2, and proton density maps were analyzed using our convolutional neural network model. To avoid overfitting on a small dataset, we aimed to separate features of images into those specific to an image and those common to the group, based on SqueezeNet. We used only common features for classification. Leave-one-out cross validation was performed to evaluate the performance of the model. The area under the receiver operating characteristic curve of the developed convolutional neural network model for differentiating between the two disorders was 0.859. The sensitivity to multiple sclerosis and neuromyelitis optica spectrum disorders, and accuracy were 80.0%, 83.3%, and 81.1%, respectively. In conclusion, we developed a convolutional neural network model that differentiates between multiple sclerosis and neuromyelitis optica spectrum disorders, and which is designed to avoid overfitting on small training datasets. Our proposed algorithm may facilitate a differential diagnosis of these diseases in clinical practice.