Patient specific prediction of temporal lobe epilepsy surgical outcomes

Benjumeda Barquita, Marco, Yen-Leng, Tan, González Otárula, Karina A., Chandramohan, Dharshan, Chang, Edward F., Hall, Jeffery A., Bielza Lozoya, María Concepción ORCID:, Larrañaga Múgica, Pedro María ORCID:, Kobayashi, Eliane and Knowlton, Robert C. (2021). Patient specific prediction of temporal lobe epilepsy surgical outcomes. "Epilepsia", v. 62 (n. 9); pp. 2113-2122. ISSN 1528-1167.


Title: Patient specific prediction of temporal lobe epilepsy surgical outcomes
  • Benjumeda Barquita, Marco
  • Yen-Leng, Tan
  • González Otárula, Karina A.
  • Chandramohan, Dharshan
  • Chang, Edward F.
  • Hall, Jeffery A.
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
  • Kobayashi, Eliane
  • Knowlton, Robert C.
Item Type: Article
Título de Revista/Publicación: Epilepsia
Date: July 2021
ISSN: 1528-1167
Volume: 62
Freetext Keywords: All epilepsy/seizures, Electroencephalography, Epilepsy surgery, Hippocampal sclerosis, Prognosis
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Objective: Drug-resistant temporal lobe epilepsy (TLE) is the most common type of epilepsy for which patients undergo surgery. Despite the best clinical judgment and currently available prediction algorithms, surgical outcomes remain variable. We aimed to build and to evaluate the performance of multidimensional Bayesian network classifiers (MBCs), a type of probabilistic graphical model, at predicting probability of seizure freedom after TLE surgery. Methods: Clinical, neurophysiological, and imaging variables were collected from 231 TLE patients who underwent surgery at the University of California, San Francisco (UCSF) or the Montreal Neurological Institute (MNI) over a 15-yearperiod. Postsurgical Engel outcomes at year 1 (Y1), Y2, and Y5 were analyzed as primary end points. We trained an MBC model on combined data sets from both institutions. Bootstrap bias corrected cross-validation (BBC-CV) was used to evaluate the performance of the models. Results: The MBC was compared with logistic regression and Cox proportional hazards according to the area under the receiver-operating characteristic curve (AUC).The MBC achieved an AUC of 0.67 at Y1, 0.72 at Y2, and 0.67 at Y5, which indicates modest performance yet superior to what has been reported in the state-of-the-art studies to date .Significance: The MBC can more precisely encode probabilistic relationships between predictors and class variables (Engel outcomes), achieving promising experimental results compared to other well-known statistical methods. Multisite application of the MBC could further optimize its classification accuracy with prospective datasets. Online access to the MBC is provided, paving the way for its use as an adjunct clinical tool in aiding pre-operative TLE surgical counseling.

Funding Projects

Government of Spain
BES-2014- 068637
Government of Spain
PID2019-109247GB- I00
Horizon 2020
Horizon 2020

More information

Item ID: 70577
DC Identifier:
OAI Identifier:
DOI: 10.1111/epi.17002
Official URL:
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 17 Feb 2023 07:27
Last Modified: 17 Feb 2023 07:27
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