Journal
FRONTIERS IN NEUROSCIENCE
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2018.01008
Keywords
focal cortical dysplasia; machine learning; metabolic; morphological; quantitative
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Funding
- Grant of Clinic and Basic Research from Capital Medical University [17JL05]
- Capital (China) Health Research and Development Special Fund [2016-1-1071]
- Beijing Municipal Science and Technology Commission [Z161100000216130, Z131107002213065]
- Beijing Municipal Administration of Hospitals' Ascent Plan [DFL20150503]
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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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