Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators.

Journal: Academic radiology
PMID: 38429188

Abstract

RATIONALE AND OBJECTIVES: To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model.

Authors

  • Zichun Yan
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Zhuowei Shi
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Qiyuan Zhu
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Jinzhou Feng
    Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yaou Liu
    Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, PR China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, PR China.
  • Yuxin Li
    University of Cincinnati, Department of Chemistry, 312 College Drive, 404 Crosley Tower, Cincinnati, Ohio 45221-0172, United States.
  • Fuqing Zhou
    Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang 330006, China; Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, China. Electronic address: fq.chou@yahoo.com.
  • Zhizheng Zhuo
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Shuang Ding
    Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing 400014, China.
  • Xiaohua Wang
    Department of Radiology, Peking University Third Hospital, Beijing, China.
  • Feiyue Yin
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yang Tang
    School of Science, Jiangsu University, Zhenjiang, China.
  • Bing Lin
    Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, No. 37 Shierqiao Avenue, Jinniu District, Chengdu 610075, China.
  • Yongmei Li
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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