AUTHOR=Mo Jia-Jie , Zhang Jian-Guo , Li Wen-Ling , Chen Chao , Zhou Na-Jing , Hu Wen-Han , Zhang Chao , Wang Yao , Wang Xiu , Liu Chang , Zhao Bao-Tian , Zhou Jun-Jian , Zhang Kai
TITLE=Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features
JOURNAL=Frontiers in Neuroscience
VOLUME=12
YEAR=2019
URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.01008
DOI=10.3389/fnins.2018.01008
ISSN=1662-453X
ABSTRACT=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.