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Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder

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机构: [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, People’s Republic of China [2]Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing 100050, People’s Republic of China [3]Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB Amsterdam, The Netherlands [4]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, People’s Republic of China [5]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China [6]University of Chinese Academy of Sciences, Beijing, China [7]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, People’s Republic of China [8]Institutes of Neurology and Healthcare Engineering, UCL, London, UK
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关键词: Magnetic resonance imaging Multiple sclerosis Neuromyelitis optica spectrum disorder Nomogram Radiomics

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Objective: To develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). Methods: We retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts. Results: Nine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort). Conclusions: A validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD. Key Points: ? Radiomic features of spinal cord lesions in MS and NMOSD were different. ? Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD. ? 2019, European Society of Radiology.

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出版当年[2018]版:
大类 | 2 区 医学
小类 | 2 区 核医学
最新[2023]版:
大类 | 2 区 医学
小类 | 2 区 核医学
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出版当年[2017]版:
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

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第一作者机构: [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, People’s Republic of China [2]Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing 100050, People’s Republic of China [3]Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB Amsterdam, The Netherlands [4]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, People’s Republic of China
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通讯机构: [1]Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, People’s Republic of China [2]Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing 100050, People’s Republic of China [3]Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, 1007 MB Amsterdam, The Netherlands [4]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, People’s Republic of China [5]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China [6]University of Chinese Academy of Sciences, Beijing, China
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