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Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology

Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serv...

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Autores principales: Ishankulov, T.A., Danilov, G.V., Pitskhelauri, D.I., Titov, O.Yu., Ogurtsova, A.A., Buklina, S.B., Gulaev, E.V., Konakova, T.A., Bykanov, A.E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Privolzhsky Research Medical University 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376754/
https://www.ncbi.nlm.nih.gov/pubmed/35992997
http://dx.doi.org/10.17691/stm2022.14.1.03
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author Ishankulov, T.A.
Danilov, G.V.
Pitskhelauri, D.I.
Titov, O.Yu.
Ogurtsova, A.A.
Buklina, S.B.
Gulaev, E.V.
Konakova, T.A.
Bykanov, A.E.
author_facet Ishankulov, T.A.
Danilov, G.V.
Pitskhelauri, D.I.
Titov, O.Yu.
Ogurtsova, A.A.
Buklina, S.B.
Gulaev, E.V.
Konakova, T.A.
Bykanov, A.E.
author_sort Ishankulov, T.A.
collection PubMed
description Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients’ quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection. MATERIALS AND METHODS: CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study. RESULTS: The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of “successful” and “unsuccessful” data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370. CONCLUSION: This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.
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spelling pubmed-93767542022-08-19 Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology Ishankulov, T.A. Danilov, G.V. Pitskhelauri, D.I. Titov, O.Yu. Ogurtsova, A.A. Buklina, S.B. Gulaev, E.V. Konakova, T.A. Bykanov, A.E. Sovrem Tekhnologii Med Advanced Researches Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients’ quality of life. The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection. MATERIALS AND METHODS: CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study. RESULTS: The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of “successful” and “unsuccessful” data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370. CONCLUSION: This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit. Privolzhsky Research Medical University 2022 2022-01-28 /pmc/articles/PMC9376754/ /pubmed/35992997 http://dx.doi.org/10.17691/stm2022.14.1.03 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Advanced Researches
Ishankulov, T.A.
Danilov, G.V.
Pitskhelauri, D.I.
Titov, O.Yu.
Ogurtsova, A.A.
Buklina, S.B.
Gulaev, E.V.
Konakova, T.A.
Bykanov, A.E.
Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology
title Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology
title_full Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology
title_fullStr Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology
title_full_unstemmed Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology
title_short Prediction of Postoperative Speech Dysfunctions in Neurosurgery Based on Cortico-Cortical Evoked Potentials and Machine Learning Technology
title_sort prediction of postoperative speech dysfunctions in neurosurgery based on cortico-cortical evoked potentials and machine learning technology
topic Advanced Researches
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376754/
https://www.ncbi.nlm.nih.gov/pubmed/35992997
http://dx.doi.org/10.17691/stm2022.14.1.03
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