Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest

Eshaghi, Arman; Wottschel, Viktor; Cortese, Rosa; Calabrese, Massimiliano; Sahraian, Mohammad Ali; Thompson, Alan; Alexander, Daniel C.; Ciccarelli, Olga

doi:10.1212/wnl.0000000000003395

Cited by 66 publications

(53 citation statements)

References 43 publications

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“…There is no definite imaging criteria to distinguish NMO from other demyelinating disorders such as MS and ADEM. [54,55] Irrelative of what is found in CC that will be discussed further in this article general distinguishing findings of MRI in such diseases are: midbrain lesions in the ventral part with poorly defined margins for ADEM vs. Medulla lesions in the dorsal part with poorly defined shape for NMO, and pons lesions with well-defined shape for MS (as the most common sites of involvement). CC involvement is more common in MS in comparison to NMO but there are also some involvement pattern differences to be pointed out.…”

Section: In Devic's Neuromyelitis Optica Mrimentioning

confidence: 92%

Neuroimaging of corpus callosum in central nervous system demyelinating disorders

Etemadifar¹,

Neshatfar²,

Zamani³

et al. 2017

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Corpus callosum (CC) is the largest white matter structure in the brain, consisting of 200-250 million contralateral axonal projections. It is the major commissural pathway connecting the hemispheres of human brain. The pathology of CC includes wide variety of entities that arise from different causes such as congenital, inflammatory, tumoral, degenerative, infectious, etc. This study reviews the most reliable neuroimaging data of human CC in central nervous system (CNS) demyelinating diseases to facilitate the understanding of different pathological entities of the CC and their role in anticipation of probable prognostic findings. After a brief description of normal anatomy and functions of CC, this review examines the most valuable findings obtained using conventional and functional magnetic resonance imaging. It also demonstrates the most well organized findings of how CC features influence prognostic factors of demyelinating disorders, which could have a great value for choosing proper therapy methods. The authors also provided a brief review of other demyelinating disorders which are primarily caused by other pathological factors other than autoimmunity. As a conclusion, the authors showed the importance of CC as an critical part of the brain, which should be explored by different methods of imaging, correspondent to clinical evaluation of CNS demyelinating disorder to widen our knowledge on pathology and clinical patterns of such disorders. Key words:Corpus callosum, central nervous system, demyelinating diseases, neuroimaging, review ABSTRACTArticle history:

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Section: In Devic's Neuromyelitis Optica Mrimentioning

confidence: 92%

Neuroimaging of corpus callosum in central nervous system demyelinating disorders

Etemadifar¹,

Neshatfar²,

Zamani³

et al. 2017

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“…2 We included patients with a secure diagnosis, meaning the disease duration on average was 7.5 years (table 1). 1 Therefore, we cannot comment on the earliest stages of NMO spectrum disorder. We previously applied a similar method to patients with clinically isolated syndrome to predict future evolution to relapsing-remitting multiple sclerosis (RRMS).…”

Section: Letter Re: Acute Zika Infection With Concurrent Onset Of Guimentioning

confidence: 98%

“…Patrick Gérardin, Saint-Pierre, Réunion; Van-Mai Cao-Lormeau, Papeete, French Polynesia; Patrice Tournebize, Saint-Pierre, Réunion; Thomas Cerny, Zurich, Switzerland: Siu et al 1 reported the concurrent onset of polyradiculoneuritis and acute Zika virus (ZIKV) infection, while the virus was not cleared from the serum. Guillain-Barré syndrome (GBS) is usually described as a postinfectious disease during which progressive flaccid paralysis develops after a phase of latency following infection.…”

Section: Letter Re: Acute Zika Infection With Concurrent Onset Of Guimentioning

confidence: 99%

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Author response: Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest

Eshaghi¹,

Ciccarelli²

2017

Neurology

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“…Andere bereits bearbeitete Fragestellungen für Radiomics-Arbeiten waren die Differenzierung von MS und Erkrankungen aus dem Neuromyelitis-Optica-Spektrum [28][29][30] und die Abgrenzung von MS-Patienten von gesunden Kontrollprobanden. Zum letztgenannten Thema existieren auch auf Deep Learning beruhende Studien [31][32][33].…”

Section: Integration Klinischer Datenunclassified

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Eichinger

Zimmer

Wiestler

2020

Rofo

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Background MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI). Methods Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019. Results There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma. Conclusion Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow. Key Points: Citation Format

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Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest

Abstract: This study provides Class II evidence that GM imaging biomarkers can distinguish patients with NMO from those with MS.

Cited by 66 publications

References 43 publications

Neuroimaging of corpus callosum in central nervous system demyelinating disorders

Neuroimaging of corpus callosum in central nervous system demyelinating disorders

Author response: Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

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