Computer-Aided Diagnosis and Localization of Lateralized Temporal Lobe Epilepsy Using Interictal FDG-PET - Kerr, Wesley T.; Nguyen, Stefan T.; Cho, Andrew Y.; Lau, Edward P.; Silverman, Daniel H.; Douglas, Pamela K.; Reddy, Navya M.; Anderson, Ariana; Bramen, Jennifer; Salamon, Noriko; Stern, John M.; Cohen, Mark S. (Frontiers in Neurology, 2013)
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. When diagnosing left temporal lobe epilepsy (LTLE) or right TLE (RTLE) versus 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 of iPET (~80%) was similar to CAD. The square correlation between our CAD tool and manual analysis, however, was only 30%, indicating that our CAD tool does not recreate manual analysis. 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.