A comparative analysis of imaging-based algorithms for detecting focal cortical dysplasia type II in children.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic. [email protected].
- Epilepsy Research Center Prague, EpiReC, Prague, Czech Republic. [email protected].
- Department of Radiology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
- Epilepsy Research Center Prague, EpiReC, Prague, Czech Republic.
- European Reference Network EpiCARE, Prague, Czech Republic.
- Department of Pathophysiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.
- Department of Pediatric Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
- Institute for Statistics and Mathematics, WU Vienna, Vienna, Austria.
- Department of Pathophysiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic. [email protected].
- Epilepsy Research Center Prague, EpiReC, Prague, Czech Republic. [email protected].
Abstract
Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy (DRE) in pediatric patients. Accurate detection of FCDs is crucial for successful surgical outcomes, yet remains challenging due to frequently subtle MRI findings, especially in children, whose brain morphology undergoes significant developmental changes. Automated detection algorithms have the potential to improve diagnostic precision, particularly in cases, where standard visual assessment fails. This study aimed to evaluate the performance of automated algorithms in detecting FCD type II in pediatric patients and to examine the impact of adult versus pediatric templates on detection accuracy. MRI data from 23 surgical pediatric patients with histologically confirmed FCD type II were retrospectively analyzed. Three imaging-based detection algorithms were applied to T1-weighted images, each targeting key structural features: cortical thickness, gray matter intensity (extension), and gray-white matter junction blurring. Their performance was assessed using adult and pediatric healthy controls templates, with validation against both predictive radiological ROIs (PRR) and post-resection cavities (PRC). The junction algorithm achieved the highest median dice score (0.028, IQR 0.038, p < 0.01 when compared with other algorithms) and detected relevant clusters even in MRI-negative cases. The adult template (median dice score 0.013, IQR 0.027) significantly outperformed the pediatric template (0.0032, IQR 0.023) (p < 0.001), highlighting the importance of template consistency. Despite superior performance of the adult template, its use in pediatric populations may introduce bias, as it does not account for age-specific morphological features such as cortical maturation and incomplete myelination. Automated algorithms, especially those targeting junction blurring, enhance FCD detection in pediatric populations. These algorithms may serve as valuable decision-support tools, particularly in settings where neuroradiological expertise is limited.