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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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.765634/full |
| _version_ | 1819127157644853248 |
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| 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 |
| collection | DOAJ |
| 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. |
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| id | doaj.art-e1ee6dae7aac4648aea7a45460f23fcd |
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| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T08:07:28Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-e1ee6dae7aac4648aea7a45460f23fcd2022-12-21T18:33:07ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-12-011510.3389/fnins.2021.765634765634Quantitative Susceptibility Mapping-Derived Radiomic Features in Discriminating Multiple Sclerosis From Neuromyelitis Optica Spectrum DisorderZichun Yan0Huan Liu1Xiaoya Chen2Qiao Zheng3Chun Zeng4Yineng Zheng5Shuang Ding6Yuling Peng7Yongmei Li8Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaGE Healthcare, Shanghai, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaObjectives: 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.https://www.frontiersin.org/articles/10.3389/fnins.2021.765634/fullmultiple sclerosisneuromyelitis optica spectrum disorderquantitative susceptibility mappingradiomicsdiscrimination |
| spellingShingle | 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 Frontiers in Neuroscience multiple sclerosis neuromyelitis optica spectrum disorder quantitative susceptibility mapping radiomics discrimination |
| title | 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_short | 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 |
| topic | multiple sclerosis neuromyelitis optica spectrum disorder quantitative susceptibility mapping radiomics discrimination |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.765634/full |
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