Surgery for temporal lobe epilepsy in children: Relevance of presurgical evaluation and analysis of outcome

"C. Munari" Epilepsy Surgery Centre and.
Journal of Neurosurgery Pediatrics (Impact Factor: 1.48). 01/2013; 11(3). DOI: 10.3171/2012.12.PEDS12334
Source: PubMed


The authors' goal in this paper was to retrospectively evaluate the relevance of the presurgical workup and the postoperative outcome in children (< 15 years) who undergo surgery for temporal lobe epilepsy (TLE).

The authors performed a retrospective analysis of 68 patients (43 boys and 25 girls) who underwent resection for TLE between 2001 and 2010 at a single center and had a minimum postoperative follow-up of 12 months. Presurgical investigations included full clinical evaluation, interictal electroencephalography (EEG), and MRI in all cases; cognitive evaluation in patients older than 5 years; scalp video-EEG in 46 patients; and invasive EEG in 3 patients. Clinical evaluation included a careful assessment of ictal semiology (based on anamnestic reports or video-EEG review), with particular attention to early signs and/or symptoms suggestive of temporal lobe origin of the seizure. Microsurgical resections were performed within the anatomical limits of the temporal lobe, and surgical specimens were processed for histological examination. Postoperative assessment of seizure outcome (Engel classification system) and cognitive performance was conducted at regular intervals. The effect on postoperative seizure outcome (good = Engel Class I; poor = Engel Classes II-IV) of several presurgical and surgical variables was investigated by bivariate statistical analysis.

All patients had at least 1 early sign or symptom suggesting a temporal lobe origin of their seizures. Lateralized interictal or ictal EEG abnormalities were seen in all patients, and they were localized to the temporal lobe in 45 patients. In all cases MRI demonstrated a structural abnormality. Surgery consisted of a tailored anterior temporal lobectomy in 64 patients and a neocortical lesionectomy in 4 patients. Postoperatively, 58 patients (85%) were in Engel Class I. Variables significantly associated with a poor outcome were preoperative sensory motor deficit (p = 0.019), mental retardation (p = 0.003), MRI abnormalities extending outside the temporal lobe (p = 0.0018), history of generalized seizures (p = 0.01) or status epilepticus (p = 0.008), unremarkable histology (p = 0.001), seizures immediately postoperatively (p = 0.00001), and ipsilateral epileptiform activity on postoperative EEG (p = 0.005). At postoperative neuropsychological assessment, the percentage of patients with a pathological score at the final visit invariably decreased compared with that at the preoperative evaluation in all considered cognitive domains.

Among the study population, a surgical selection based on a noninvasive evaluation was possible in most patients. The invaluable information resulting from the rigorous noninvasive electroclinical and neuroimaging evaluation can lead to excellent surgical results without the use of invasive, time-consuming, and expensive diagnostic tools. The potential reduction of invasiveness-related risks, complexity, and costs of presurgical investigations should hopefully allow for an increase in the number of children with TLE who will receive surgery, particularly in centers with limited technological resources.

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Available from: Lino Nobili, Apr 13, 2014
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    ABSTRACT: Objectives: In the presurgical analysis for drug-resistant focal epilepsies, the definition of the epileptogenic zone, which is the cortical area where ictal discharges originate, is usually carried out by using clinical, electrophysiological and neuroimaging data analysis. Clinical evaluation is based on the visual detection of symptoms during epileptic seizures. This work aims at developing a fully automatic classifier of epileptic types and their localization using ictal symptoms and machine learning methods. Methods: We present the results achieved by using two machine learning methods. The first is an ontology-based classification that can directly incorporate human knowledge, while the second is a genetics-based data mining algorithm that learns or extracts the domain knowledge from medical data in implicit form. Results: The developed methods are tested on a clinical dataset of 129 patients. The performance of the methods is measured against the performance of seven clinicians, whose level of expertise is high/very high, in classifying two epilepsy types: temporal lobe epilepsy and extra-temporal lobe epilepsy. When comparing the performance of the algorithms with that of a single clinician, who is one of the seven clinicians, the algorithms show a slightly better performance than the clinician on three test sets generated randomly from 99 patients out of the 129 patients. The accuracy obtained for the two methods and the clinician is as follows: first test set 65.6% and 75% for the methods and 56.3% for the clinician, second test set 66.7% and 76.2% for the methods and 61.9% for the clinician, and third test set 77.8% for the methods and the clinician. When compared with the performance of the whole population of clinicians on the rest 30 patients out of the 129 patients, where the patients were selected by the clinicians themselves, the mean accuracy of the methods (60%) is slightly worse than the mean accuracy of the clinicians (61.6%). Results show that the methods perform at the level of experienced clinicians, when both the methods and the clinicians use the same information. Conclusion: Our results demonstrate that the developed methods form important ingredients for realizing a fully automatic classification of epilepsy types and can contribute to the definition of signs that are most important for the classification.
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