Observational studyEpilepsia2021
Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy.
Observational study in Epilepsia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Automated preoperative volumetric analysis as a marker of the seizure irritative zone.Acta neurochirurgica · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- Seminars in epileptology: Presurgical epilepsy evaluation.Epileptic disorders : international epilepsy journal with videotape · 2025Review
- Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.Scientific reports · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- The predicative value of early quantitative electroencephalograph in epilepsy after severe traumatic brain injury in children.Frontiers in pediatrics · 2024Article
- Identifying important factors for successful surgery in patients with lateral temporal lobe epilepsy.PloS one · 2023Article
- Entering the Era of Personalized Medicine in Epilepsy Through Neuroimaging Machine Learning.Epilepsy currents · 2022Article
- ResectVol: A tool to automatically segment and characterize lacunas in brain images.Epilepsia open · 2021Article
- Machine Learning for Precision Epilepsy Surgery.Epilepsy currentsReview
- Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future.Epilepsy currentsArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectivePatients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy.
methodsNinety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom.
resultsForty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non-seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897. SIGNIFICANCE: This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.
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