Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder

  • Yaou LiuEmail author
  • Di Dong
  • Liwen Zhang
  • Yali Zang
  • Yunyun Duan
  • Xiaolu Qiu
  • Jing Huang
  • Huiqing Dong
  • Frederik Barkhof
  • Chaoen Hu
  • Mengjie Fang
  • Jie TianEmail author
  • Kuncheng Li
Magnetic Resonance



To develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).


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.


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).


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.


Multiple sclerosis Neuromyelitis optica spectrum disorder Radiomics Nomogram Magnetic resonance imaging 



Areas under the ROC curves


Expanded disability status scale


Least absolute shrinkage and selection operator


Longitudinal extensive transverse myelitis


Multiple sclerosis


Neuromyelitis optica spectrum disorder


Receiver operating characteristic


Region of interest


Relapsing-remitting MS



We thank our patients in this study and members of the neuroimmunology team and staffs of the department of radiology for various supports.


This work was supported by the ECTRIMS-MAGNMIS Fellowship from ECTRIMS (Y.L), the National Science Foundation of China (Nos. 81101038, 81227901, 81771924, 81501736, 61231004, 81401377, 81471221 and 81230028), the National Basic Research Program of China (2013CB966900), National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1308701), the Beijing Natural Science fund (No.7133244), the Beijing Nova Programme (xx2013045), Beijing Municipal Administration of Hospital Clinical Medicine Development of Special Funding Support (code:ZYLX201609), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), and Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (2012BAI10B04).

Compliance with ethical standards


The scientific guarantor of this publication is Yaou Liu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6026_MOESM1_ESM.doc (1.3 mb)
ESM 1 (DOC 1333 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Yaou Liu
    • 1
    • 2
    • 3
    • 4
    Email author
  • Di Dong
    • 5
    • 6
  • Liwen Zhang
    • 5
    • 6
  • Yali Zang
    • 5
    • 6
  • Yunyun Duan
    • 1
    • 2
  • Xiaolu Qiu
    • 4
  • Jing Huang
    • 4
  • Huiqing Dong
    • 7
  • Frederik Barkhof
    • 3
    • 8
  • Chaoen Hu
    • 5
    • 6
  • Mengjie Fang
    • 5
    • 6
  • Jie Tian
    • 5
    • 6
    Email author
  • Kuncheng Li
    • 1
    • 4
  1. 1.Department of Radiology, Beijing Tiantan HospitalCapital Medical UniversityBeijingPeople’s Republic of China
  2. 2.Tiantan Image Research CenterChina National Clinical Research Center for Neurological DiseasesBeijingPeople’s Republic of China
  3. 3.Department of Radiology and Nuclear Medicine, Neuroscience Campus AmsterdamVU University Medical CenterAmsterdamThe Netherlands
  4. 4.Department of Radiology, Xuanwu HospitalCapital Medical UniversityBeijingPeople’s Republic of China
  5. 5.CAS Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  6. 6.University of Chinese Academy of SciencesBeijingChina
  7. 7.Department of Neurology, Xuanwu HospitalCapital Medical UniversityBeijingPeople’s Republic of China
  8. 8.Institutes of Neurology and Healthcare Engineering, UCLLondonUK

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