Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome. Issue 3 (16th July 2021) - Morita-Sherman, Marcia; Li, Manshi; Joseph, Boney; Yasuda, Clarissa; Vegh, Deborah; De Campos, Brunno Machado; Alvim, Marina K M; Louis, Shreya; Bingaman, William; Najm, Imad; Jones, Stephen; Wang, Xiaofeng; et al.
Abstract: Quantitative volumetric brain MRI measurement is important in research applications, but translating it into patient care is challenging. We explore the incorporation of clinical automated quantitative MRI measurements in statistical models predicting outcomes of surgery for temporal lobe epilepsy. Four hundred and thirty-five patients with drug-resistant epilepsy who underwent temporal lobe surgery at Cleveland Clinic, Mayo Clinic and University of Campinas were studied. We obtained volumetric measurements from the pre-operative T1-weighted MRI using NeuroQuant, a Food and Drug Administration approved software package. We created sets of statistical models to predict the probability of complete seizure-freedom or an Engel score of I at the last follow-up. The cohort was randomly split into training and testing sets, with a ratio of 7:3. Model discrimination was assessed using the concordance statistic (C-statistic). We compared four sets of models and selected the one with the highest concordance index. Volumetric differences in pre-surgical MRI located predominantly in the frontocentral and temporal regions were associated with poorer outcomes. The addition of volumetric measurements to the model with clinical variables alone increased the model's C-statistic from 0.58 to 0.70 (right-sided surgery) and from 0.61 to 0.66 (left-sided surgery) for complete seizure freedom and from 0.62 to 0.67 (right-sided surgery) and from 0.68 to 0.73 (left-sided surgery) for an Engel I outcome score. 57% of patients with extra-temporal abnormalities were seizure-free at last follow-up, compared to 68% of those with no such abnormalities ( P -value = 0.02). Adding quantitative MRI data increases the performance of a model developed to predict post-operative seizure outcomes. The distribution of the regions of interest included in the final model supports the notion that focal epilepsies are network disorders and that subtle cortical volume loss outside the surgical site influences seizure outcome. Abstract : Abbreviated summary Morita-Sherman et al. demonstrate how sophisticated research imaging can cross the divide to clinical practice. They show how quantitative MRI can enhance epilepsy surgery outcome prediction. Volume changes in the structures highlighted were identified as outcome predictors, supporting the hypothesis that subtle cortical atrophy outside the resection influences seizure outcome. Graphical Abstract: