18F-FDG PET Imaging Analysis of Antiepileptic Drug Response in BECTS

18F-FDG PET Imaging Analysis of Antiepileptic Drug Response in Benign Epilepsy With Centrotemporal Spikes Patients

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04357236

Enrollment

Registered

2020-04-22

Start date

2019-06-01

Completion date

2020-04-20

Last updated

2020-04-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Benign Epilepsy With Centrotemporal Spikes

Keywords

Benign Epilepsy with Centrotemporal Spikes, glucose metabolism, positron emission tomography, machine learning

Brief summary

This original article is a novel investigation on the metabolic characteristics of different patterns of antiepileptic drug (AED) responses in benign epilepsy with centrotemporal spikes (BECTS) patients using 18F-FDG PET imaging. In this study, we demonstrated remitting-relapsing group showed more widespread hypo-metabolism regions than AED responders. Results indicated that metabolic differences had the ability to distinguish the remitting-relapsing patients from AED responders. 18F-FDG PET could be used as a marker to infer the current seizure activity of BECTS. We think that the established hybrid model based on PET and clinical features may be a critical reference for better personalized medication in patients with BECTS.

Detailed description

Purpose The current drug treatment of benign epilepsy with centrotemporal spikes (BECTS) mainly depends on the clinical experience of physicians. This study aimed to investigate different patterns of antiepileptic drug (AED) responses in patients with BECTS using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging for better personalized medication. Methods A total of 55 patients with BECTS (36 AED responders, 19 remitting-relapsing patients) and 23 pseudo-controls who underwent 18F-FDG PET imaging were retrospectively included. The group comparison was performed to investigate metabolic differences among AED responders, remitting-relapsing patients and pseudo-controls. Three different logistic regression models were employed to distinguish remitting-relapsing patients from AED responders based on clinical features, 18F-FDG PET images and a hybrid of both. Ten AED responders who reduced AED dose and one remitting-relapsing patient who relapsed within one month after PET examination were included in the model evaluation.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

6 Years to 18 Years

Healthy volunteers

Inclusion criteria

* 1.clinical diagnosis of BECTS; 2. aging between 6 and 18 years ; 3.taking MRI and EEG examination; 4. taking AEDs as prescribed; 5. continuous 12-month clinical follow-up after 18F-FDG PET examination; 6.the last seizure occurring earlier than 24 h before 18F-FDG PET study

Exclusion criteria

* 1.any history of neurological disorders, such as head trauma, tumor or infarct

Design outcomes

Primary

Measure	Time frame	Description
The 'area under curve' (AUC ) of our model in classification performance	Through study completion, about 6 months	To evaluate the performance of our model, the investigators calculated the AUC of three different logistic regression models based on clinical features, 18F-FDG PET images and a hybrid of both.

Countries

China

Outcome results