Dataset Information

Epilepsy in glioma patients: mechanisms, management, and impact of anticonvulsant therapy.

ABSTRACT: Seizures are a well-recognized symptom of primary brain tumors, and anticonvulsant use is common. This paper provides an overview of epilepsy and the use of anticonvulsants in glioma patients. Overall incidence and mechanisms of epileptogenesis are reviewed. Factors to consider with the use of antiepileptic drugs (AEDs) including incidence during the disease trajectory and prophylaxis along with considerations in the selection of anticonvulsant use (ie, potential side effects, drug interactions, adverse effects, and impact on survival) are also reviewed. Finally, areas for future research and exploring the pathophysiology and use of AEDs in this population are also discussed.

SUBMITTER: Armstrong TS

PROVIDER: S-EPMC4864254 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Epilepsy in glioma patients: mechanisms, management, and impact of anticonvulsant therapy.

Armstrong Terri S TS Grant Robin R Gilbert Mark R MR Lee Jong Woo JW Norden Andrew D AD

Neuro-oncology 20151102 6

Seizures are a well-recognized symptom of primary brain tumors, and anticonvulsant use is common. This paper provides an overview of epilepsy and the use of anticonvulsants in glioma patients. Overall incidence and mechanisms of epileptogenesis are reviewed. Factors to consider with the use of antiepileptic drugs (AEDs) including incidence during the disease trajectory and prophylaxis along with considerations in the selection of anticonvulsant use (ie, potential side effects, drug interactions, ...[more]

PMID: 26527735

Similar Datasets

Project description:ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical-radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong's test was used to compare the performance of the models. A two-sided t-test and Fisher's exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance.ResultsThe classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical-radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819-0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707-0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively.ConclusionRadiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical-radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

Project description:Epilepsy is a chronic neurologic disorder that affects over 70 million people worldwide. Despite the availability of over 20 antiseizure drugs (ASDs) for symptomatic treatment of epileptic seizures, about one-third of patients with epilepsy have seizures refractory to pharmacotherapy. Patients with such drug-resistant epilepsy (DRE) have increased risks of premature death, injuries, psychosocial dysfunction, and a reduced quality of life, so development of more effective therapies is an urgent clinical need. However, the various types of epilepsy and seizures and the complex temporal patterns of refractoriness complicate the issue. Furthermore, the underlying mechanisms of DRE are not fully understood, though recent work has begun to shape our understanding more clearly. Experimental models of DRE offer opportunities to discover, characterize, and challenge putative mechanisms of drug resistance. Furthermore, such preclinical models are important in developing therapies that may overcome drug resistance. Here, we will review the current understanding of the molecular, genetic, and structural mechanisms of ASD resistance and discuss how to overcome this problem. Encouragingly, better elucidation of the pathophysiological mechanisms underpinning epilepsies and drug resistance by concerted preclinical and clinical efforts have recently enabled a revised approach to the development of more promising therapies, including numerous potential etiology-specific drugs ("precision medicine") for severe pediatric (monogenetic) epilepsies and novel multitargeted ASDs for acquired partial epilepsies, suggesting that the long hoped-for breakthrough in therapy for as-yet ASD-resistant patients is a feasible goal. SIGNIFICANCE STATEMENT: Drug resistance provides a major challenge in epilepsy management. Here, we will review the current understanding of the molecular, genetic, and structural mechanisms of drug resistance in epilepsy and discuss how the problem might be overcome.

Dataset Information

Epilepsy in glioma patients: mechanisms, management, and impact of anticonvulsant therapy.

Publications

Epilepsy in glioma patients: mechanisms, management, and impact of anticonvulsant therapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets