Focal cortical dysplasia: etiology, epileptogenesis, classification, clinical presentation, imaging, and management

Tahta, Alican; Turğut, Mehmet

doi:10.1007/s00381-020-04851-9

Cited by 17 publications

(14 citation statements)

References 72 publications

(150 reference statements)

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“…The concept of FCD type Ⅲ can be defined as FCD type I adjacent to another principal lesion, such as hippocampal sclerosis (HS), low-grade developmental epilepsy-associated tumors (LDEATs), or vascular malformations [ 6 ]. The pathogenesis of FCD type Ⅲ is uncertain, because it is hard to tell whether the cortical dyslamination is primary or secondary [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Focal Cortical Dysplasia Type Ⅲ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome

Wang

Deng

Zhou³

et al. 2021

Diagnostics

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This study aims to explore the relationship between neuropathologic and the post-surgical prognosis of focal cortical dysplasia (FCD) typed-Ⅲ-related medically refractory epilepsy. A total of 266 patients with FCD typed-Ⅲ-related medically refractory epilepsy were retrospectively studied. Presurgical clinical data, type of surgery, and postsurgical seizure outcome were analyzed. The minimum post-surgical follow-up was 1 year. A total of 266 patients of FCD type Ⅲ were included in this study and the median follow-up time was 30 months (range, 12~48 months). Age at onset ranged from 1.0 years to 58.0 years, with a median age of 12.5 years. The number of patients under 12 years old was 133 (50%) in patients with FCD type Ⅲ. A history of febrile seizures was present in 42 (15.8%) cases. In the entire postoperative period, 179 (67.3%) patients were seizure-free. Factors with p < 0.15 in univariate analysis, such as age of onset of epilepsy (p = 0.145), duration of epilepsy (p = 0.004), febrile seizures (p = 0.150), being MRI-negative (p = 0.056), seizure type (p = 0.145) and incomplete resection, were included in multivariate analysis. Multivariate analyses revealed that MRI-negative findings of FCD (OR 0.34, 95% CI 0.45–0.81, p = 0.015) and incomplete resection (OR 0.12, 95% CI 0.05–0.29, p < 0.001) are independent predictors of unfavorable seizure outcomes. MRI-negative finding of FCD lesions and incomplete resection were the most important predictive factors for poor seizure outcome in patients with FCD type Ⅲ.

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Section: Introductionmentioning

confidence: 99%

Focal Cortical Dysplasia Type Ⅲ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome

Wang

Deng

Zhou³

et al. 2021

Diagnostics

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“…[6] The complicated interaction between neurons, and imbalance in inhibitory and excitatory neurotransmitters are known mechanisms of epileptogenesis in FCD; with overexpression of AMPA and NMDA receptor subunits and loss of GABAergic neurons. [7,8] Many studies have recently suggested that focal epilepsy is not a limited to a speci c lesion, but results in alterations of the whole-brain network, due to the characteristic feature of the brain as a complex and interconnected system. [9,10] However, little is known about the structural connectivity or network in patients with FCD.…”

Section: Introductionmentioning

confidence: 99%

Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia

Lee

Kim

et al. 2021

Preprint

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Background The aim of this study was to investigate alterations in structural connectivity and structural co-variance network in patients with focal cortical dysplasia (FCD). Methods We enrolled 37 patients with FCD and 35 healthy controls. All subjects underwent brain MRI with the same scanner and with the same protocol, which included diffusion tensor imaging (DTI) and T1-weighted imaging. We analyzed the structural connectivity based on DTI, and structural co-variance network based on the structural volume with T1-weighted imaging. We created a connectivity matrix and obtained network measures from the matrix using the graph theory. We tested the difference in network measure between patients with FCD and healthy controls. Results In the structural connectivity analysis, we found that the local efficiency in patients with FCD was significantly lower than in healthy controls (2.390 vs. 2.578, p = 0.031). Structural co-variance network analysis revealed that the mean clustering coefficient, global efficiency, local efficiency, and transitivity were significantly decreased in patients with FCD compared to those in healthy controls (0.527 vs. 0.635, p = 0.036; 0.545 vs. 0.648, p = 0.026; 2.699 vs. 3.801, p = 0.019; 0.791 vs. 0.954, p = 0.026, respectively). Conclusion We demonstrate that there are significant alterations in structural connectivity, based on DTI, and structural co-variance network, based on the structural volume, in patients with FCD compared to healthy controls. These findings suggest that focal lesions with FCD could affect the whole-brain network and that FCD is a network disease.

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“…In 1971, Taylor first described focal cortical dysplasia (FCD), which is characterized by disordered neurons in white matter, dyslamination, and aberrant balloon cells, and can induce drug-resistant or intractable epilepsy. Approximately half (46.5%) of drug-resistant epileptic patients have pathologically confirmed abnormalities ( 1 , 2 ). The International League Against Epilepsy (ILAE) suggested the first international categorization of FCD in 2011 ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

“…FCD type III comprises 4subgroups based on the pathophysiology of the accompanying lesions, specifically hippocampal sclerosis (FCD type IIIa), low-grade developmental epilepsy-associated tumors (LDEATs) (FCD type IIIb), vascular lesions (FCD type IIIc), and scar/hypoxic damage (FCD type IIId). ( 1 , 5 , 6 ).…”

Section: Introductionmentioning

confidence: 99%

Developing a deep learning model to predict epilepsy recurrence in patients with focal cortical dysplasia type III

Wang¹,

Zhou²,

Deng³

et al. 2023

Quant Imaging Med Surg

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Background: A sizable number of patients with focal cortical dysplasia (FCD) type III-related refractory epilepsy continue to experience seizures postsurgically. Deep learning models can automatically assess complex medical image characteristics and predict prognosis with higher efficiency. This study sought to determine whether T2-weighted fluid attenuated inversion recovery (T2W FLAIR) images could predict prognosis of FCD type III-related refractory epilepsy using a deep learning approach.Methods: Magnetic resonance imaging (MRI) images of 266 patients with FCD type III diagnosed between 2015 and 2019 were included in this retrospective analysis. A deep learning algorithm utilizing a convolutional neural network (CNN) was trained to classify T2W FLAIR images according to Engel's classification. The preprocessed original image and the region of interest (ROI) outlined by clinicians were input into our neural network separately and then together. Precision, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curves (AUCs) were computed as part of the statistical analyses of the network performance with varied inputs of the network model assessed. Results:The overall performance met the following metrics when the original image only was input: AUC of 96.22%, sensitivity of 84.47%, and specificity of 97.21%. The metrics were as follows when the ROI only was input: area under the ROC curve of 94.76%, sensitivity of 84.92%, and specificity of 96.24%. For the combined inputs, the metrics were as follows: AUC of 97.17%, sensitivity of 90.86%, and specificity of 96.63%.Conclusions: Deep learning used with conventional MRI can effectively predict the recurrence conditions of epilepsy. Artificial intelligence may help the design of clinical management and enable more precise and individualized prediction for postsurgical prognosis of FCD type III-related refractory epilepsy.

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Focal cortical dysplasia: etiology, epileptogenesis, classification, clinical presentation, imaging, and management

Cited by 17 publications

References 72 publications

Focal Cortical Dysplasia Type Ⅲ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome

Focal Cortical Dysplasia Type Ⅲ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome

Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia

Developing a deep learning model to predict epilepsy recurrence in patients with focal cortical dysplasia type III

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