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.

Highlights

  • It is difficult to differentiate between patients with epilepsy (PWE), and those with non-epileptic seizures (NES)

  • Considering that one sixth of PWE are diagnosed with medication refractory epilepsy (Privitera, 2011), improved methods to effectively identify patients with NES (PWN) who do not benefit from anti-epileptic drugs (AEDs) effectively could reduce the morbidity and both the financial and social cost of treating epilepsy

  • The binary computer-aided diagnostic (CAD) tool for right TLE (RTLE) had accuracy of 88% (69–90%), compared to the accuracy of manual analysis (MA) of Interictal FDG-PET (iPET) [85% (72–92%)] and structural MRI (sMRI) [77% (63–85)]

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Summary

Introduction

It is difficult to differentiate between patients with epilepsy (PWE), and those with non-epileptic seizures (NES). Long term video-EEG monitoring has shown consistently that roughly one third of patients diagnosed with “medication refractory epilepsy” suffer from NES (Kerr et al, 2012a). Because they don’t suffer from epilepsy, these patients with NES (PWN) are not treated effectively with anti-epileptic drugs (AEDs). A minority of PWN suffers from organic, non-epileptic maladies that can be confused with seizure disorder including, but not limited to, dementia and cardiovascular disease (Sahaya et al, 2011). Considering that one sixth of PWE are diagnosed with medication refractory epilepsy (Privitera, 2011), improved methods to effectively identify PWN who do not benefit from AEDs effectively could reduce the morbidity and both the financial and social cost of treating epilepsy

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