Application of deep-learning to the seronegative side of the NMO spectrum

Cacciaguerra, Laura; Storelli, Loredana; Radaelli, Marta; Mesaroš, Šarlota; Moiola, Lucia; Drulović, Jelena; Filippi, Massimo; Rocca, Maria A.

doi:10.1007/s00415-021-10727-y

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…Previous studies have used only brain or spinal cord images ( 11 , 20 ), along with accompanying clinical variables ( 18 , 33 ) for prediction. Among these studies, Rocca et al.…”

Section: Discussionmentioning

confidence: 99%

“…In conventional magnetic resonance imaging (MRI), bilateral periventricular white matter and cortical lesions are often considered typical features of MS ( 9 ), whereas longitudinally extensive transverse myelitis and posterior long-segment optic nerve lesions are more specific to NMOSD ( 10 ). MOGAD is considered to exhibit intermediate MRI features between those of MS and NMOSD ( 11 ). However, the MRI manifestations in some cases are indistinguishable among these conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Recent advances in artificial intelligence have prompted the development of deep learning-based algorithms designed for the automatic classification of demyelinating diseases based on conventional MRI ( 11 , 18 – 20 ). For example, Kim et al.…”

Section: Introductionmentioning

confidence: 99%

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Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging

Huang

Chen²,

Liu³

et al. 2022

Front. Immunol.

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BackgroundDifferential diagnosis of demyelinating diseases of the central nervous system is a challenging task that is prone to errors and inconsistent reading, requiring expertise and additional examination approaches. Advancements in deep-learning-based image interpretations allow for prompt and automated analyses of conventional magnetic resonance imaging (MRI), which can be utilized in classifying multi-sequence MRI, and thus may help in subsequent treatment referral.MethodsImaging and clinical data from 290 patients diagnosed with demyelinating diseases from August 2013 to October 2021 were included for analysis, including 67 patients with multiple sclerosis (MS), 162 patients with aquaporin 4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD), and 61 patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model. This novel transformer-based deep-learning model architecture was designed to be versatile in handling with multiple image sequences (coronal T2-weighted and sagittal T2-fluid attenuation inversion recovery) and scanning locations (brain and spinal cord) for differentiating among MS, NMOSD, and MOGAD. Model performances were evaluated using the area under the receiver operating curve (AUC) and the confusion matrices measurements. The classification accuracy between the fusion model and the neuroradiological raters was also compared.ResultsThe fusion model that was trained with combined brain and spinal cord MRI achieved an overall improved performance, with the AUC of 0.933 (95%CI: 0.848, 0.991), 0.942 (95%CI: 0.879, 0.987) and 0.803 (95%CI: 0.629, 0.949) for MS, AQP4+ NMOSD, and MOGAD, respectively. This exceeded the performance using the brain or spinal cord MRI alone for the identification of the AQP4+ NMOSD (AUC of 0.940, brain only and 0.689, spinal cord only) and MOGAD (0.782, brain only and 0.714, spinal cord only). In the multi-category classification, the fusion model had an accuracy of 81.4%, which was significantly higher compared to rater 1 (64.4%, p=0.04<0.05) and comparable to rater 2 (74.6%, p=0.388).ConclusionThe proposed novel transformer-based model showed desirable performance in the differentiation of MS, AQP4+ NMOSD, and MOGAD on brain and spinal cord MRI, which is comparable to that of neuroradiologists. Our model is thus applicable for interpretating conventional MRI in the differential diagnosis of demyelinating diseases with overlapping lesions.

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“…Previous studies have used only brain or spinal cord images ( 11 , 20 ), along with accompanying clinical variables ( 18 , 33 ) for prediction. Among these studies, Rocca et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transformer-Based Deep-Learning Algorithm for Discriminating Demyelinating Diseases of the Central Nervous System With Neuroimaging

Huang

Chen²,

Liu³

et al. 2022

Front. Immunol.

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“…A deep-learning algorithm trained with brain MRI scans of AQP4-IgG+ NMOSD and MS patients was also validated to be able to forecast whether seronegative NMOSD-like manifestations would be developed into antibody-positive NMOSD or MS or stay DN NMOSD. 41 …”

Section: Brain Lesionsmentioning

confidence: 99%

Double-negative neuromyelitis optica spectrum disorder

Wu,

Geraldes,

Juryńczyk

et al. 2023

Mult Scler

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Most patients with neuromyelitis optica spectrum disorders (NMOSD) test positive for aquaporin-4 antibody (AQP4-IgG) or myelin oligodendrocyte glycoprotein antibodies (MOG-IgG). Those who are negative are termed double-negative (DN) NMOSD and may constitute a diagnostic and therapeutic challenge. DN NMOSD is a syndrome rather than a single disease, ranging from a (postinfectious) monophasic illness to a more chronic syndrome that can be indistinguishable from AQP4-IgG+ NMOSD or develop into other mimics such as multiple sclerosis. Thus, underlying disease mechanisms are likely to be heterogeneous. This topical review aims to (1) reappraise antibody-negative NMOSD definition as it has changed over time with the development of the AQP4 and MOG-IgG assays; (2) outline clinical characteristics and the pathophysiological nature of this rare entity by contrasting its differences and similarities with antibody-positive NMOSD; (3) summarize laboratory characteristics and magnetic resonance imaging findings of DN NMOSD; and (4) discuss the current treatment for DN NMOSD.

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