Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases.

Journal: Journal of translational medicine
PMID: 34488799

Abstract

BACKGROUND: Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO.

Authors

  • Jing Huang
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Bowen Xin
    School of Information Technologies, The University of Sydney, Sydney, NSW, Australia.
  • Xiuying Wang
    Otolaryngology Department, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Zhigang Qi
    Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xuanwu District, Beijing, 100053, China.
  • Huiqing Dong
    Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Kuncheng Li
    Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China.
  • Yun Zhou
    MOE Key Lab of Environmental and Occupational Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430030, China.
  • Jie Lu
    Department of Endocrinology and Metabolism, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.

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