Incorporation of quantitative MRI in a model to predict temporal lobe epilepsy surgery outcome

Author:

Morita-Sherman Marcia1ORCID,Li Manshi2,Joseph Boney3ORCID,Yasuda Clarissa4,Vegh Deborah1,De Campos Brunno Machado4,Alvim Marina K M4,Louis Shreya1ORCID,Bingaman William1ORCID,Najm Imad1,Jones Stephen1,Wang Xiaofeng2,Blümcke Ingmar5ORCID,Brinkmann Benjamin H3ORCID,Worrell Gregory3,Cendes Fernando4,Jehi Lara1

Affiliation:

1. Department of Neurology, Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA

2. Department of Quantitative Health Sciences, Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA

3. Department of Neurology, Mayo Clinic, Rochester, MN, USA

4. Department of Neurology, University of Campinas, Campinas, Brazil

5. Department of Neuropathology, University Hospitals, Erlangen, Germany

Abstract

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.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

General Earth and Planetary Sciences,General Environmental Science

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