Clinical value of machine learning in the automated detection of focal cortical dysplasia using quantitative multimodal surface-based features
JJ Mo, JG Zhang, WL Li, C Chen, NJ Zhou… - Frontiers in …, 2019 - frontiersin.org
JJ Mo, JG Zhang, WL Li, C Chen, NJ Zhou, WH Hu, C Zhang, Y Wang, X Wang, C Liu…
Frontiers in neuroscience, 2019•frontiersin.orgObjective: 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 …
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 …
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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