Abstracts

Rationale:
Frontal lobectomy can be a successful surgical intervention for patients with medically refractory frontal epilepsy albeit with lower rates of seizure freedom when compare to temporal lobectomy and several other resective interventions.  Research has traditionally focused on identifying clinical prognostic indicators to improve patient selection in patients with frontal lobe epilepsy, but we hypothesized that extrafrontal or global volume loss could be a structural correlate for a more widespread epilepsy network or epilepsy mislocalized to the frontal lobe, and thus could be a negative prognostic indicator for seizure freedom. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy.
Method:
Ninety patients who underwent frontal lobectomy were stratified based on seizure freedom at mean follow-up time of 3.5 (std. dev. 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted MRIs. Cortical volumes were determined as percentiles compared to age, sex, and cranial volume-matched controls using FDA-approved software (NeuroQuant, Cortech Labs, San Diego, California). Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom.
Results:
Forty-eight of 90 (53.3%) patients demonstrated seizure freedom at last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right side in the seizure free and non-seizure free cohorts (Table 1). A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes developed a model with a c-statistic of 0.897. Figure 1 demonstrates a heat-map of the structures identified by the model as seizure outcome predictors based on univariate analysis of preoperative volume loss and volume asymmetry.
Conclusion:
This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using only preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy and with further studies may be developed for integration into preoperative risk stratification. To the best of our knowledge, this is the first study to demonstrate a structural correlate between cortical volume loss in a proposed frontal lobe epilepsy network and seizure outcomes in patients who underwent frontal lobectomy.
Funding:
:Research reported is supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number 1RO1NS097719-01A1.