Focal cortical dysplasia diagnosis [Neurosurgery Wiki]

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====== Focal cortical dysplasia diagnosis ======

{{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1bCr63ThlO2ZEjaZuSo_ARBVSMstSVMT9ej7C7DLH_g5Vk497D/?limit=15&utm_campaign=pubmed-2&fc=20250225061040}}
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FCD is challenging to visualize and often considered magnetic resonance imaging (MRI) negative. Existing automated methods for FCD detection are limited by high numbers of false-positive predictions, hampering their clinical utility.

The diagnosis of [[Focal cortical dysplasia]] can often be strongly suspected based on a detailed history and [[physical examination]]. FCD often causes seizures that onset in the first 5 years of life, and the majority will have seizures by the age of 16 years. Less commonly, seizures can start in adulthood. The subtype of FCD correlates to some degree with age at onset, with FCD Type II presenting most commonly very early in childhood, and some cases of  FCD type III presenting later in life. In addition to seizures, FCD may result in clinical symptoms that result from focal disruption of brain function in the region affected by the dysplasia, such as language delays, weakness, or visual concerns.
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The electroencephalogram (EEG) may show slowing of the background activity in the region of the FCD, however, this finding is not specific for FCD and can be seen with other causes of epilepsy. More suggestive of FCD is abnormal focal fast activity. Epileptiform discharges are also commonly seen. 

===== Magnetic resonance imaging =====

[[Focal cortical dysplasia magnetic resonance imaging]].


===== MEG =====

[[Magnetoencephalography for focal cortical dysplasia diagnosis]].

===== Multicenter diagnostic studies =====

Ripart et al. evaluated the efficacy and interpretability of graph neural networks in automatically detecting FCD lesions on MRI scans.

In this multicenter diagnostic study, retrospective MRI data were collated from 23 [[epilepsy center]]s worldwide between 2018 and 2022, as part of the Multicenter Epilepsy Lesion Detection (MELD) Project, and analyzed in 2023. Data from 20 centers were split equally into training and testing cohorts, with data from 3 centers withheld for site-independent testing. A graph neural network (MELD Graph) was trained to identify FCD on surface-based features. Network performance was compared with an existing algorithm. Feature analysis, saliencies, and confidence scores were used to interpret network predictions. In total, 34 surface-based MRI features and manual lesion masks were collated from participants, 703 patients with FCD-related epilepsy and 482 controls, and 57 participants were excluded during MRI quality control.

Main outcomes and measures: Sensitivity, specificity, and positive predictive value (PPV) of automatically identified lesions.

Results: In the test dataset, the MELD Graph had a sensitivity of 81.6% in histopathologically confirmed patients seizure-free 1 year after surgery and 63.7% in MRI-negative patients with FCD. The PPV of putative lesions from the 260 patients in the test dataset (125 female [48%] and 135 male [52%]; mean age, 18.0 [IQR, 11.0-29.0] years) was 67% (70% sensitivity; 60% specificity), compared with 39% (67% sensitivity; 54% specificity) using an existing baseline algorithm. In the independent test cohort (116 patients; 62 female [53%] and 54 male [47%]; mean age, 22.5 [IQR, 13.5-27.5] years), the PPV was 76% (72% sensitivity; 56% specificity), compared with 46% (77% sensitivity; 47% specificity) using the baseline algorithm. Interpretable reports characterize lesion location, size, confidence, and salient features.

Conclusions and relevance: In this study, the MELD Graph represented a state-of-the-art, openly available, and interpretable tool for FCD detection on MRI scans with significant improvements in PPV. Its clinical implementation holds promise for early diagnosis and improved management of focal epilepsy, potentially leading to better patient outcomes
((Ripart M, Spitzer H, Williams LZJ, Walger L, Chen A, Napolitano A, Rossi-Espagnet C, Foldes ST, Hu W, Mo J, Likeman M, Rüber T, Caligiuri ME, Gambardella A, Guttler C, Tietze A, Lenge M, Guerrini R, Cohen NT, Wang I, Kloster A, Pinborg LH, Hamandi K, Jackson G, Tortora D, Tisdall M, Conde-Blanco E, Pariente JC, Perez-Enriquez C, Gonzalez-Ortiz S, Mullatti N, Vecchiato K, Liu Y, Kalviainen R, Sokol D, Shetty J, Sinclair B, Vivash L, Willard A, Winston GP, Yasuda C, Cendes F, Shinohara RT, Duncan JS, Cross JH, Baldeweg T, Robinson EC, Iglesias JE, Adler S, Wagstyl K; MELD FCD writing group; Fawaz A, De Benedictis A, De Palma L, Zhang K, Labate A, Barba C, You X, Gaillard WD, Tang Y, Wang S, Davies S, Semmelroch M, Severino M, Striano P, Chari A, D'Arco F, Mankad K, Bargallo N, Pascual-Diaz S, Delgado-Martinez I, O'Muircheartaigh J, Abela E, Kandasamy J, McLellan A, Desmond P, Lui E, O'Brien TJ, Whitaker K. Detection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks: A MELD Study. JAMA Neurol. 2025 Feb 24. doi: 10.1001/jamaneurol.2024.5406. Epub ahead of print. PMID: 39992650.))