Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy.
Cross, J Helen
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Adler, S., Wagstyl, K., Gunny, R., Ronan, L., Carmichael, D., Cross, J. H., Fletcher, P., & et al. (2016). Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy.. NeuroImage: Clinical, 14 18-27. https://doi.org/10.1016/j.nicl.2016.12.030
Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort. We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the "doughnut" method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features. Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.
AUC, area under the curve, Automated classification, FCD, FCD, focal cortical dysplasia, FLAIR, fluid-attenuated inversion recovery, Intractable epilepsy, LCD, local cortical deformation, LGI, local gyrification index, PCA, principal component analysis, Paediatric, ROC, receiver operator characteristic, Structural MRI, Adolescent, Area Under Curve, Brain Mapping, Cerebral Cortex, Child, Child, Preschool, Epilepsy, Female, Humans, Imaging, Three-Dimensional, Machine Learning, Magnetic Resonance Imaging, Male, Malformations of Cortical Development, Group I, Oxygen
Is supplemented by: https://doi.org/10.17863/CAM.6923
This research was supported by the National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. SA received funding from the Rosetrees Trust (A711). KW received funding from the James Baird Fund and the Wellcome Trust (WT095692MA). TB from Great Ormond Street Hospital Children's Charity (V1213 and V2416). LR and PCF are funded by the Wellcome Trust and the Bernard Wolfe Health Neuroscience Fund.
Wellcome Trust (095692/Z/11/Z)
External DOI: https://doi.org/10.1016/j.nicl.2016.12.030
This record's URL: https://www.repository.cam.ac.uk/handle/1810/277127
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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