Preoperative prediction of temporal lobe epilepsy surgery outcome

Goldenholz, Daniel M.; Jow, Alexander; Khan, Omar; Bagić, Anto; Satô, Susumu; Auh, Sungyoung; Kufta, C.; Inati, Sara K.; Theodore, William H.

doi:10.1016/j.eplepsyres.2016.09.015

Cited by 22 publications

(22 citation statements)

References 43 publications

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“…In agreement with our results, other studies have shown seizure free in the range of 60–70% after temporal lobectomy [ 21 , 22 , 23 ]. The proportion of patients free of seizure and aura over the long time in the current study (57.7%) was similar compared to a recent report that also showed complete seizure freedom in 65% of patients at 1year and in 56.5% at long-term follow-up of ≥5 years after temporal lobectomy [ 24 ]. Another author that included temporal and extratemporal resections detailed that >80% of the patients experienced class I outcome at the last four-year follow-up [ 25 ].…”

Section: Discussionsupporting

confidence: 87%

“…This pattern also anticipated a favorable clinical evolution five years postsurgery; and has been associated with good outcomes in patients with TLE in earlier studies [ 28 , 45 ]. Nowadays, the controversy about the relative contributions of ictal scalp VEEG, routine scalp outpatient interictal EEG, intracranial EEG and MRI for predicting seizure-free outcomes after temporal lobectomy still remains [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

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Long-Term Electroclinical and Employment Follow up in Temporal Lobe Epilepsy Surgery. A Cuban Comprehensive Epilepsy Surgery Program

Chacón

Maeso

Martín

et al. 2018

Behavioral Sciences

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The purpose of this paper is to present a long- term electroclinical and employment follow up in temporal lobe epilepsy (TLE) patients in a comprehensive epilepsy surgery program. Forty adult patients with pharmacoresistant TLE underwent detailed presurgical evaluation. Electroencephalogram (EEG) and clinical follow up assessment for each patient were carried out. The occurrence of interictal epileptiform activity (IEA) and absolute spike frequency (ASF) were tabulated before and after 1, 6, 12, 24 and 72 months surgical treatment. Employment status pre- to post-surgery at the last evaluated period was also examined. Engel scores follow-up was described as follows: at 12 months 70% (28) class I, 10% (4) class II and 19% (8) class III-IV; at 24 months after surgery 55.2% (21) of the patients were class I, 28.9% (11) class II and 15.1% (6) class III-IV. After one- year follow up 23 (57.7%) patients were seizure and aura-free (Engel class IA). These figures changed to 47.3%, and 48.6% respectively two and five years following surgery whereas 50% maintained this condition in the last follow up period. A decline in the ASF was observed from the first year until the sixth year after surgery in relation to the preoperative EEG. The ASF one year after surgery allowed to distinguish “satisfactory” from “unsatisfactory” seizure relief outcome at the last follow up. An adequate social functioning in terms of education and employment in more than 50% of the patients was also found. Results revealed the feasibility of conducting a successful epilepsy surgery program with favorable long term electroclinical and psychosocial functioning outcomes in a developing country as well.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Long-Term Electroclinical and Employment Follow up in Temporal Lobe Epilepsy Surgery. A Cuban Comprehensive Epilepsy Surgery Program

Chacón

Maeso

Martín

et al. 2018

Behavioral Sciences

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“…Longitudinal studies have shown that surgery outcome varies over time and that the number of patients achieving seizure freedom decreases over several years ( 52 , 53 ). The estimation of surgery outcome at a different time point would likely have resulted in different groups and therefore possibly different results ( 54 ). Factors such as long-term tissue transformation or tumor growth can change surgery outcome at different time points ( 55 ).…”

Section: Discussionmentioning

confidence: 99%

Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients

et al. 2018

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Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom.Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups.Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67–60.22%) for SVM and 60.34% (59.98–60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08–45.45%) for SVM and 49.03% (47.25–50.82%) for random forest].Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.

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“…surgery. This will necessarily be dependent on the individual's clinical characteristics, imaging findings and interictal EEG abnormalities [30,31] A recent review concluded VEM findings failed to predict outcome at 6 months, 1 year and 2 years in temporal lobe epilepsy when corrected for routine EEG and MRI findings [32], and markers other than VEM findings, such as imaging may be more reliable in predicting postoperative seizure remission [33]. In pre-surgical planning VEM data must be reviewed in an epilepsy surgery MDT alongside all clinical information, imaging and neuropsychology.…”

Section: A View From the Cess (Children's Epilepsy Surgery Service)mentioning

confidence: 99%

Current practice and recommendations in UK epilepsy monitoring units. Report of a national survey and workshop

Hamandi

Beniczky²,

Diehl

et al. 2017

Seizure

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Preoperative prediction of temporal lobe epilepsy surgery outcome

Cited by 22 publications

References 43 publications

Long-Term Electroclinical and Employment Follow up in Temporal Lobe Epilepsy Surgery. A Cuban Comprehensive Epilepsy Surgery Program

Long-Term Electroclinical and Employment Follow up in Temporal Lobe Epilepsy Surgery. A Cuban Comprehensive Epilepsy Surgery Program

Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients

Current practice and recommendations in UK epilepsy monitoring units. Report of a national survey and workshop

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