Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET

Abstract

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy (TLE). When diagnosing left TLE (LTLE) or right TLE (RTLE) vs. non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% [95% confidence interval (CI) 69–90%] or 88% (95% CI 76–94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66–84%), where 89% (95% CI 77–96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement – not replace – expert analysis. In our dataset, the accuracy of manual analysis (MA) of iPET (∼80%) was similar to CAD. The square correlation between our CAD tool and MA, however, was only 30%, indicating that our CAD tool does not recreate MA. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.

MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features

“…The ample evidences of advanced research using multimodal combinational non-invasive neuroimaging techniques like MRI, MRS, f-MRI, FDG-PET, SPECT, and MEG have revealed that most of the cases of TLE patients are accompanied by pathological changes in brain structure and function network, including cortical and subcortical (hippocampus and parahippocampus) structures [12][13][14]. Such multimodal combinational imaging (structural and functional) approaches are being suggested to detect the laterization of TLE with ictal and interictal onset in pre-surgical diagnostic purpose [15][16][17][18][19][20]. Regardless, epilepsy occasionally is a clinical problem as regards medical management and the problem becomes acute in developing countries as there is often delay in clinical diagnosis due to the limited resources, large patient load, and health care manpower constraints [21][22][23].…”

Machine learning classification of mesial temporal sclerosis in epilepsy patients

“…Furthermore, as we only utilized T1 MRI data and did not incorporate signal variation from T2/FLAIR scans (Coan et al, 2014, Cantor-Rivera et al, 2015 or alterations found in PET (Kerr et al, 2013) or DTI data (Cantor-Rivera et al, 2015), it is clear that there is still much room for improving classifier performance. Thus, in the future, an even more comprehensive model that identifies subgroups, incorporates other imaging modalities in addition to clinical and neuropsychological data, would likely be superior in regards to both diagnosis and predicting treatment outcomes, thus achieving clinical utility.…”

Quantification of Task-Specific Glucose Metabolism with Constant Infusion of18F-FDG

“…These include tumor imaging (1), neurologic diseases such as epilepsy (2) and Alzheimer disease (3)(4)(5), and psychiatric disorders such as major depression (6). Hence, PET using the radiolabeled glucose analog 18 F-FDG offers an excellent opportunity for imaging and quantification of glucose metabolism.…”