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Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In...

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Autores principales: Lamichhane, Bidhan, Daniel, Andy G. S., Lee, John J., Marcus, Daniel S., Shimony, Joshua S., Leuthardt, Eric C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937731/
https://www.ncbi.nlm.nih.gov/pubmed/33692747
http://dx.doi.org/10.3389/fneur.2021.642241
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author Lamichhane, Bidhan
Daniel, Andy G. S.
Lee, John J.
Marcus, Daniel S.
Shimony, Joshua S.
Leuthardt, Eric C.
author_facet Lamichhane, Bidhan
Daniel, Andy G. S.
Lee, John J.
Marcus, Daniel S.
Shimony, Joshua S.
Leuthardt, Eric C.
author_sort Lamichhane, Bidhan
collection PubMed
description Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.
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spelling pubmed-79377312021-03-09 Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients Lamichhane, Bidhan Daniel, Andy G. S. Lee, John J. Marcus, Daniel S. Shimony, Joshua S. Leuthardt, Eric C. Front Neurol Neurology Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients. Frontiers Media S.A. 2021-02-22 /pmc/articles/PMC7937731/ /pubmed/33692747 http://dx.doi.org/10.3389/fneur.2021.642241 Text en Copyright © 2021 Lamichhane, Daniel, Lee, Marcus, Shimony and Leuthardt. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Lamichhane, Bidhan
Daniel, Andy G. S.
Lee, John J.
Marcus, Daniel S.
Shimony, Joshua S.
Leuthardt, Eric C.
Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
title Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
title_full Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
title_fullStr Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
title_full_unstemmed Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
title_short Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
title_sort machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937731/
https://www.ncbi.nlm.nih.gov/pubmed/33692747
http://dx.doi.org/10.3389/fneur.2021.642241
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