ArticleBrain communications2021
Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome.
Article in Brain communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- 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
- Geometric representations of brain networks can predict the surgery outcome in temporal lobe epilepsy.NPJ systems biology and applications · 2025Article
- MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).PloS one · 2025Article
- Epilepsy Therapies Symposium | Do We Really "Outgrow" Seizures?Epilepsy currents · 2024Review
- 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
- Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data?Epilepsia · 2023Article
- Interictal MEG abnormalities to guide intracranial electrode implantation and predict surgical outcome.ArXiv · 2023Article
- Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome.Brain communications · 2023Article
- Molecular subtypes of epilepsy associated with post-surgical seizure recurrence.Brain communications · 2023Article
- Epilepsy in Pediatric Patients-Evaluation of Brain Structures' Volume Using VolBrain Software.Journal of clinical medicine · 2022Article
- Genetic and molecular features of seizure-freedom following surgical resections for focal epilepsy: A pilot study.Frontiers in neurology · 2022Article
- Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuantFrontiers in human neuroscience · 2022Review
- Amygdala subnuclear volumes in temporal lobe epilepsy with hippocampal sclerosis and in non-lesional patients.Brain communications · 2022Article
- Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps.Epilepsy currentsReview
- Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future.Epilepsy currentsArticle
- Machine Learning for Precision Epilepsy Surgery.Epilepsy currentsReview
Corrections and comments
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Authors and funding
17 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model's C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities (
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