Background and Objective: Epilepsy affects approximately 50 million people worldwide, with 30-40% of patients not responding to medication, necessitating alternative therapies such as surgical intervention. However, the accurate localization of epileptogenic lesions, particularly in pediatric magnetic resonance imaging (MRI)-negative drug-resistant epilepsy, remains a challenge. This paper reviews advanced neuroimaging techniques aimed at improving the detection of such lesions to enhance surgical outcomes. Methods: A comprehensive literature search was conducted using PubMed, focusing on advanced MRI sequences, focal epilepsy, and the integration of artificial intelligence (AI) in the diagnostic process. Key Content and Findings: New MRI sequences, including magnetization prepared 2 rapid gradient echo (MP2RAGE), edge-enhancing gradient echo (EDGE), and fluid and white matter suppression (FLAWS), have demonstrated enhanced capabilities in detecting subtle epileptogenic lesions. Quantitative MRI techniques, notably magnetic resonance fingerprinting (MRF), alongside innovative post-processing methods, are emphasized for their effectiveness in delineating cortical malformations, whether used alone or in combination with ultra-high field MRI systems. Furthermore, the integration of AI in radiology is progressing, providing significant support in accurately localizing lesions, and potentially optimizing presurgical planning. Conclusions: While advanced neuroimaging and AI offer significant improvements in the diagnostic process for epilepsy, some challenges remain. These include long acquisition times, the need for extensive data analysis, and a lack of large, standardized datasets for AI validation. However, the future holds promise as research continues to integrate these technologies into clinical practice. These efforts will improve the clinical applicability and effectiveness of these advanced techniques in epilepsy management, paving the way for more accurate diagnoses and better patient outcomes.
Pastore, L.V., De Vita, E., Sudhakar, S.V., Lobel, U., Mankad, K., Biswas, A., et al. (2024). Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review. TRANSLATIONAL PEDIATRICS, 13(9), 1617-1633 [10.21037/tp-24-166].
Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review
Cirillo L.;
2024
Abstract
Background and Objective: Epilepsy affects approximately 50 million people worldwide, with 30-40% of patients not responding to medication, necessitating alternative therapies such as surgical intervention. However, the accurate localization of epileptogenic lesions, particularly in pediatric magnetic resonance imaging (MRI)-negative drug-resistant epilepsy, remains a challenge. This paper reviews advanced neuroimaging techniques aimed at improving the detection of such lesions to enhance surgical outcomes. Methods: A comprehensive literature search was conducted using PubMed, focusing on advanced MRI sequences, focal epilepsy, and the integration of artificial intelligence (AI) in the diagnostic process. Key Content and Findings: New MRI sequences, including magnetization prepared 2 rapid gradient echo (MP2RAGE), edge-enhancing gradient echo (EDGE), and fluid and white matter suppression (FLAWS), have demonstrated enhanced capabilities in detecting subtle epileptogenic lesions. Quantitative MRI techniques, notably magnetic resonance fingerprinting (MRF), alongside innovative post-processing methods, are emphasized for their effectiveness in delineating cortical malformations, whether used alone or in combination with ultra-high field MRI systems. Furthermore, the integration of AI in radiology is progressing, providing significant support in accurately localizing lesions, and potentially optimizing presurgical planning. Conclusions: While advanced neuroimaging and AI offer significant improvements in the diagnostic process for epilepsy, some challenges remain. These include long acquisition times, the need for extensive data analysis, and a lack of large, standardized datasets for AI validation. However, the future holds promise as research continues to integrate these technologies into clinical practice. These efforts will improve the clinical applicability and effectiveness of these advanced techniques in epilepsy management, paving the way for more accurate diagnoses and better patient outcomes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
