Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features.

Journal: Frontiers in neuroscience
Published Date: Jan 11, 2019

Abstract

To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value. Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis. The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes ( = 0.001, = 0.970). between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair). Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring.

Authors

  • Jia-Jie Mo
    Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Jian-Guo Zhang
    Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Wen-Ling Li
    Department of Functional Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Na-Jing Zhou
    Department of Pharmacology, Hebei Medical University, Shijiazhuang, China.
  • Wen-Han Hu
    Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Yao Wang
    Department of Gastrointestinal Surgery, Zhongshan People's Hospital, Zhongshan, Guangdong, China.
  • Xiu Wang
    Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Bao-Tian Zhao
    Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Jun-Jian Zhou
    Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.

Keywords

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