Predicting surgical outcome in temporal lobe epilepsy patients using MRI and MRSI

Objective: To develop a classifier that uses MR data to predict surgical outcome in patients with temporal lobe epilepsy (TLE). Methods: Eighty-one patients with medically refractory TLE who underwent surgical treatment were studied. Patients underwent comprehensive presurgical investigation, including ictal video-EEG recording, H-1 MRS imaging, and volumetric MRI. Outcome was measured using Engel's classification system, condensed into two outcome groups. Two approaches were taken. First, outcome was defined as experiencing worthwhile improvement with >90% reduction of seizure frequency (Classes I, II, and III) or not (Class IV). A second approach was to define outcome as experiencing freedom from seizures following surgery (Class I) or not (Classes II, III, and IV). For each approach, a Bayesian classifier was constructed to predict outcome by calculating the probability of a patient's pattern of results from spectroscopic analysis of the temporal lobes and volumetric analysis of the amygdala and hippocampus being associated with the various outcome groups. Results: The worthwhile improvement classifier correctly predicted the surgical outcomes of 60 of 65 (92%) of patients who experienced worthwhile improvement and 10 of 16 (63%) of patients who did not. The seizure-free classifier correctly predicted the surgical outcomes of 39 of 52 (75%) of patients who became seizure free and 21 of 29 (72%) of patients who did not. Conclusion: MR features are important markers of surgical outcome in patients with TLE and can provide assistance in identifying surgical candidates.