Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder

Objectives: To implement a machine learning model using radiomic features extracted from quantitative susceptibility mapping (QSM) in discriminating multiple sclerosis (MS) from neuromyelitis optica spectrum disorder (NMOSD).Materials and Methods: Forty-seven patients with MS (mean age = 40.00 ± 13....

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Autores principales:	Zichun Yan, Huan Liu, Xiaoya Chen, Qiao Zheng, Chun Zeng, Yineng Zheng, Shuang Ding, Yuling Peng, Yongmei Li
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	multiple sclerosis neuromyelitis optica spectrum disorder quantitative susceptibility mapping radiomics discrimination Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/e1ee6dae7aac4648aea7a45460f23fcd
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spelling	oai:doaj.org-article:e1ee6dae7aac4648aea7a45460f23fcd2021-12-03T07:19:44ZQuantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder1662-453X10.3389/fnins.2021.765634https://doaj.org/article/e1ee6dae7aac4648aea7a45460f23fcd2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.765634/fullhttps://doaj.org/toc/1662-453XObjectives: To implement a machine learning model using radiomic features extracted from quantitative susceptibility mapping (QSM) in discriminating multiple sclerosis (MS) from neuromyelitis optica spectrum disorder (NMOSD).Materials and Methods: Forty-seven patients with MS (mean age = 40.00 ± 13.72 years) and 36 patients with NMOSD (mean age = 42.14 ± 12.34 years) who underwent enhanced gradient-echo T2*-weighted angiography (ESWAN) sequence in 3.0-T MRI were included between April 2017 and October 2019. QSM images were reconstructed from ESWAN, and QSM-derived radiomic features were obtained from seven regions of interest (ROIs), including bilateral putamen, globus pallidus, head of the caudate nucleus, thalamus, substantia nigra, red nucleus, and dentate nucleus. A machine learning model (logistic regression) was applied to classify MS and NMOSD, which combined radiomic signatures and demographic information to assess the classification accuracy using the area under the receiver operating characteristic (ROC) curve (AUC).Results: The radiomics-only models showed better discrimination performance in almost all deep gray matter (DGM) regions than the demographic information-only model, with the highest AUC in DN of 0.902 (95% CI: 0.840–0.955). Moreover, the hybrid model combining radiomic signatures and demographic information showed the highest discrimination performance which achieved the AUC of 0.927 (95% CI: 0.871–0.984) with fivefold cross-validation.Conclusion: The hybrid model based on QSM and powered with machine learning has the potential to discriminate MS from NMOSD.Zichun YanHuan LiuXiaoya ChenQiao ZhengChun ZengYineng ZhengShuang DingYuling PengYongmei LiFrontiers Media S.A.articlemultiple sclerosisneuromyelitis optica spectrum disorderquantitative susceptibility mappingradiomicsdiscriminationNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multiple sclerosis neuromyelitis optica spectrum disorder quantitative susceptibility mapping radiomics discrimination Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	multiple sclerosis neuromyelitis optica spectrum disorder quantitative susceptibility mapping radiomics discrimination Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Zichun Yan Huan Liu Xiaoya Chen Qiao Zheng Chun Zeng Yineng Zheng Shuang Ding Yuling Peng Yongmei Li Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder
description	Objectives: To implement a machine learning model using radiomic features extracted from quantitative susceptibility mapping (QSM) in discriminating multiple sclerosis (MS) from neuromyelitis optica spectrum disorder (NMOSD).Materials and Methods: Forty-seven patients with MS (mean age = 40.00 ± 13.72 years) and 36 patients with NMOSD (mean age = 42.14 ± 12.34 years) who underwent enhanced gradient-echo T2*-weighted angiography (ESWAN) sequence in 3.0-T MRI were included between April 2017 and October 2019. QSM images were reconstructed from ESWAN, and QSM-derived radiomic features were obtained from seven regions of interest (ROIs), including bilateral putamen, globus pallidus, head of the caudate nucleus, thalamus, substantia nigra, red nucleus, and dentate nucleus. A machine learning model (logistic regression) was applied to classify MS and NMOSD, which combined radiomic signatures and demographic information to assess the classification accuracy using the area under the receiver operating characteristic (ROC) curve (AUC).Results: The radiomics-only models showed better discrimination performance in almost all deep gray matter (DGM) regions than the demographic information-only model, with the highest AUC in DN of 0.902 (95% CI: 0.840–0.955). Moreover, the hybrid model combining radiomic signatures and demographic information showed the highest discrimination performance which achieved the AUC of 0.927 (95% CI: 0.871–0.984) with fivefold cross-validation.Conclusion: The hybrid model based on QSM and powered with machine learning has the potential to discriminate MS from NMOSD.
format	article
author	Zichun Yan Huan Liu Xiaoya Chen Qiao Zheng Chun Zeng Yineng Zheng Shuang Ding Yuling Peng Yongmei Li
author_facet	Zichun Yan Huan Liu Xiaoya Chen Qiao Zheng Chun Zeng Yineng Zheng Shuang Ding Yuling Peng Yongmei Li
author_sort	Zichun Yan
title	Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder
title_short	Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder
title_full	Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder
title_fullStr	Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder
title_full_unstemmed	Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder
title_sort	quantitative susceptibility mapping-derived radiomic features in discriminating multiple sclerosis from neuromyelitis optica spectrum disorder
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/e1ee6dae7aac4648aea7a45460f23fcd
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Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum Disorder

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