Application of preoperative MRI lesion identification algorithm in pediatric and young adult focal cortical dysplasia-related epilepsy

Authors

Kara L. Hom, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States.
Venkata Sita Illapani, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States.
Hua Xie, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States.
Chima Oluigbo, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States.
L Gilbert Vezina, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States.
William D. Gaillard, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States.
Taha Gholipour, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States; Department of Neurosciences, University of California San Diego, San Diego, CA, United States.
Nathan T. Cohen, Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, United States. Electronic address: ncohen@childrensnational.org.

Document Type

Journal Article

Publication Date

11-1-2024

Journal

Seizure

Volume

122

DOI

10.1016/j.seizure.2024.09.024

Keywords

Automated MRI segmentation; Automated detection; Focal cortical dysplasia; Machine learning; Non-lesional focal epilepsy; Structural MRI

Abstract

OBJECTIVE: The purpose of this study was to evaluate the performance and generalizability of an automated, interpretable surface-based MRI classifier for the detection of focal cortical dysplasia. METHODS: This was a retrospective cohort incorporating MRIs from the epilepsy surgery (FCD and MRI-negative) and neuroimaging (healthy controls) databases at Children's National Hospital (CNH), and a publicly-available FCD Type II dataset from Bonn, Germany. Clinical characteristics and outcomes were abstracted from patient records and/or existing databases. Subjects were included if they had 3T epilepsy-protocol MRI. Manually-segmented FCD masks were compared to the automated masks generated by the Multi-centre Epilepsy Lesion Detection (MELD) FCD detection algorithm. Sensitivity/specificity were calculated. RESULTS: From CNH, 39 FCD pharmacoresistant epilepsy (PRE) patients, 19 healthy controls, and 19 MRI-negative patients were included. From Bonn, 85 FCD Type II were included, of which 68 passed preprocessing. MELD had varying performance (sensitivity) in these datasets: CNH FCD-PRE (54 %); Bonn (68 %); MRI-negative (44 %). In multivariate regression, FCD Type IIB pathology predicted higher chance of MELD automated lesion detection. All four patients who underwent resection/ablation of MELD-identified clusters achieved Engel I outcome. SIGNIFICANCE: We validate the performance of MELD automated, interpretable FCD classifier in a diverse pediatric cohort with FCD-PRE. We also demonstrate the classifier has relatively good performance in an independent FCD Type II cohort with pediatric-onset epilepsy, as well as simulated real-world value in a pediatric population with MRI-negative PRE.

APA Citation

Hom, Kara L.; Illapani, Venkata Sita; Xie, Hua; Oluigbo, Chima; Vezina, L Gilbert; Gaillard, William D.; Gholipour, Taha; and Cohen, Nathan T., "Application of preoperative MRI lesion identification algorithm in pediatric and young adult focal cortical dysplasia-related epilepsy" (2024). GW Authored Works. Paper 6070.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/6070

Department

Neurology