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Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS). Material and Methods: 122 student athletes (65 M, 57 F)...

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Autores principales: Tamez-Peña, José, Rosella, Peter, Totterman, Saara, Schreyer, Edward, Gonzalez, Patricia, Venkataraman, Arun, Meyers, Steven P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784748/
https://www.ncbi.nlm.nih.gov/pubmed/35082743
http://dx.doi.org/10.3389/fneur.2021.734329
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author Tamez-Peña, José
Rosella, Peter
Totterman, Saara
Schreyer, Edward
Gonzalez, Patricia
Venkataraman, Arun
Meyers, Steven P.
author_facet Tamez-Peña, José
Rosella, Peter
Totterman, Saara
Schreyer, Edward
Gonzalez, Patricia
Venkataraman, Arun
Meyers, Steven P.
author_sort Tamez-Peña, José
collection PubMed
description Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS). Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15–20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury. Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01). Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions.
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spelling pubmed-87847482022-01-25 Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning Tamez-Peña, José Rosella, Peter Totterman, Saara Schreyer, Edward Gonzalez, Patricia Venkataraman, Arun Meyers, Steven P. Front Neurol Neurology Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS). Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15–20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury. Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01). Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions. Frontiers Media S.A. 2022-01-10 /pmc/articles/PMC8784748/ /pubmed/35082743 http://dx.doi.org/10.3389/fneur.2021.734329 Text en Copyright © 2022 Tamez-Peña, Rosella, Totterman, Schreyer, Gonzalez, Venkataraman and Meyers. https://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
Tamez-Peña, José
Rosella, Peter
Totterman, Saara
Schreyer, Edward
Gonzalez, Patricia
Venkataraman, Arun
Meyers, Steven P.
Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning
title Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning
title_full Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning
title_fullStr Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning
title_full_unstemmed Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning
title_short Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning
title_sort post-concussive mtbi in student athletes: mri features and machine learning
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784748/
https://www.ncbi.nlm.nih.gov/pubmed/35082743
http://dx.doi.org/10.3389/fneur.2021.734329
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