Frontiers in Neuroscience (Jan 2019)

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

  • Jia-Jie Mo,
  • Jian-Guo Zhang,
  • Wen-Ling Li,
  • Chao Chen,
  • Na-Jing Zhou,
  • Wen-Han Hu,
  • Chao Zhang,
  • Yao Wang,
  • Xiu Wang,
  • Chang Liu,
  • Bao-Tian Zhao,
  • Jun-Jian Zhou,
  • Kai Zhang

DOI
https://doi.org/10.3389/fnins.2018.01008
Journal volume & issue
Vol. 12

Abstract

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Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value.Methods: 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.Results: 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 (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair).Conclusion: 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.

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