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

prediction of epilepsy surgery outcome quantitative MRI temporal lobe epilepsy volumetric measurements

Journal

Brain communications
ISSN: 2632-1297
Titre abrégé: Brain Commun
Pays: England
ID NLM: 101755125

Informations de publication

Date de publication:
2021
Historique:
accepted: 01 06 2021
entrez: 16 8 2021
pubmed: 17 8 2021
medline: 17 8 2021
Statut: epublish

Résumé

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 (

Identifiants

pubmed: 34396113
doi: 10.1093/braincomms/fcab164
pii: fcab164
pmc: PMC8361423
doi:

Types de publication

Journal Article

Langues

eng

Pagination

fcab164

Informations de copyright

© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.

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Auteurs

Marcia Morita-Sherman (M)

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

Manshi Li (M)

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

Boney Joseph (B)

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

Clarissa Yasuda (C)

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

Deborah Vegh (D)

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

Brunno Machado De Campos (BM)

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

Marina K M Alvim (MKM)

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

Shreya Louis (S)

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

William Bingaman (W)

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

Imad Najm (I)

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

Stephen Jones (S)

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

Xiaofeng Wang (X)

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

Ingmar Blümcke (I)

Department of Neuropathology, University Hospitals, Erlangen, Germany.

Benjamin H Brinkmann (BH)

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

Gregory Worrell (G)

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

Fernando Cendes (F)

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

Lara Jehi (L)

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

Classifications MeSH