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. 2024 Nov:122:64-70.
doi: 10.1016/j.seizure.2024.09.024. Epub 2024 Sep 30.

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

Kara L Hom  1 , Venkata Sita Priyanka Illapani  1 , Hua Xie  1 , Chima Oluigbo  1 , L Gilbert Vezina  1 , William D Gaillard  1 , Taha Gholipour  2 , Nathan T Cohen  3
Affiliations

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

Kara L Hom et al. Seizure. 2024 Nov.

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.

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

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1A:
Figure 1A:. MELD Classifier Overall Performance Across Datasets
This graph shows the sensitivities of the MELD algorithm across different datasets: gold column (unadjusted sensitivity in the overall original MELD manuscript, gold standard); red column (overall CNH Test cohort); salmon column (subgroup of CNH Test cohort with age <3 years at scan); gray column (Bonn cohort); black column (CNH MRI-Negative cohort).
Figure 1B:
Figure 1B:. MELD Classifier Performance by FCD Subtype
This graph demonstrates the reported sensitivities across FCD pathologic subtypes from the original MELD algorithm (gold column), as well as the sensitivities calculated across datasets (red, overall CNH Test cohort; salmon, CNH Test <3y; gray, Bonn) and stratified by FCD subtype (when available). Of note, for FCD Type I, the CNH Test <3y group had 0% sensitivity (no MELD clusters detected in one patient with FCD Type I).
See this image and copyright information in PMC

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