Diagnostic performance of neuroimaging modalities for epileptogenic focus localization: A systematic review
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Notice bibliographique
Résumé
OBJECTIVE: Accurate localization of epileptogenic foci remains of significant importance for surgical planning in drug-resistant epilepsy. Multiple neuroimaging modalities are available; however, their comparative diagnostic performance lacks comparative detailed synthesis. This systematic review aimed to evaluate and compare the diagnostic accuracy of structural MRI, PET imaging, SPECT/SISCOM, and combined multimodal strategies for epileptogenic focus localization. METHODS: We conducted a systematic review following PRISMA 2020 guidelines, searching PubMed, Scopus, Google Scholar, Cochrane Library, and Web of Science databases up to May 30, 2025. Studies evaluating the diagnostic performance of neuroimaging modalities for epilepsy focus localization with surgical correlation were included. Data extraction focused on sensitivity, specificity, and clinical manner. Quality assessment used QUADAS-2 criteria. RESULTS: Fifteen studies included a total of 1157 patients that met inclusion criteria. Combined multimodal strategies integrating two or more imaging modalities demonstrated the highest diagnostic performance (sensitivity 82-100%), followed by structural MRI in lesional epilepsy (72-100% sensitivity). PET imaging showed consistent performance across clinical contexts (33-89% sensitivity), while SPECT/SISCOM exhibited variable results (33-83% sensitivity). Strong complementarity existed between MRI and PET (85% concordance), with context-dependent optimization for lesional versus non-lesional epilepsy. SIGNIFICANCE: Combined multimodal neuroimaging provides superior diagnostic performance for epileptogenic focus localization. Clinical context significantly impacts the modality selection, with MRI prioritized in lesional cases and functional imaging essential for MRI-negative epilepsy. These findings support evidence-based imaging protocols for surgical epilepsy evaluation. PLAIN LANGUAGE SUMMARY: This systematic review evaluated which brain imaging techniques are best for finding the exact location where seizures start in people with drug-resistant epilepsy who need surgery. The researchers analyzed 15 studies involving 1157 patients. They found that using multiple imaging techniques together (combining structural and functional imaging) provides the most accurate results, with success rates of 82-100%. Standard MRI scans work very well (72-100% accuracy) when there is a visible brain abnormality causing seizures. However, for patients whose MRI looks normal, additional functional imaging techniques like PET or SPECT scans are crucial, achieving 63-89% accuracy. The study shows that the best imaging approach depends on the individual patient's situation: MRI should be used first when a brain lesion is suspected, but functional imaging becomes essential when MRI does not show anything abnormal. These findings help doctors choose the right combination of imaging tests for each patient to improve surgical planning and outcomes.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle