Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder
- 206 Downloads
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.
• Radiomic features of spinal cord lesions in MS and NMOSD were different.
• Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD.
KeywordsMultiple 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
Neuromyelitis optica spectrum disorder
Receiver operating characteristic
Region of interest
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.
Written informed consent was obtained from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
- 5.Thompson AJ, Banwell BL, Barkhof F et al (2017) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173Google Scholar
- 15.Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol 58:267–288Google Scholar
- 27.Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34(18):2157–2164Google Scholar
- 28.Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269Google Scholar