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Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy

Background: Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but add...

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Autores principales: Akinci D'Antonoli, Tugba, Santini, Francesco, Deligianni, Xeni, Garcia Alzamora, Meritxell, Rutz, Erich, Bieri, Oliver, Brunner, Reinald, Weidensteiner, Claudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8019698/
https://www.ncbi.nlm.nih.gov/pubmed/33828520
http://dx.doi.org/10.3389/fneur.2021.633808
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author Akinci D'Antonoli, Tugba
Santini, Francesco
Deligianni, Xeni
Garcia Alzamora, Meritxell
Rutz, Erich
Bieri, Oliver
Brunner, Reinald
Weidensteiner, Claudia
author_facet Akinci D'Antonoli, Tugba
Santini, Francesco
Deligianni, Xeni
Garcia Alzamora, Meritxell
Rutz, Erich
Bieri, Oliver
Brunner, Reinald
Weidensteiner, Claudia
author_sort Akinci D'Antonoli, Tugba
collection PubMed
description Background: Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but additional methods for characterizing muscle composition have not thoroughly been investigated. MRI fat fraction (FF) measurement can quantify muscle fat and is often a part of standard imaging in neuromuscular dystrophies. To date, FF has been used to quantify muscle fat and assess function in CP. In this study, we aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones. Materials and Methods: A total of 9 patients (age range 8–15 years) with CP and 12 healthy controls (age range 9–16 years) were prospectively enrolled (2018–2020) after ethics committee approval. Multi-echo Dixon acquisition of the calf muscles was used for FF calculation. The images of the second echo (TE = 2.87 ms) were used for feature extraction from the soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles. The least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. RM, FF model (FFM), and combined model (CM) were built for each calf muscle. The receiver operating characteristic (ROC) curve and their respective area under the curve (AUC) values were used to evaluate model performance. Results: In total, the affected legs of 9 CP patients and the dominant legs of 12 healthy controls were analyzed. The performance of RM for soleus, gastrocnemius medialis, and gastrocnemius lateralis (AUC 0.92, 0.92, 0.82, respectively) was better than the FFM (AUC 0.88, 0.85, 0.69, respectively). The combination of both models always had a better performance than RM or FFM (AUC 0.95, 0.93, 0.83). FF was higher in the patient group (FF(S) 9.1%, FF(GM) 8.5%, and FF(GL) 10.2%) than control group (FF(S) 3.3%, FF(GM) 4.1%, FF(GL) 6.6%). Conclusion: The combination of MRI quantitative fat fraction analysis and texture analysis of muscles is a promising tool to evaluate muscle pathologies due to CP in a non-invasive manner.
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spelling pubmed-80196982021-04-06 Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy Akinci D'Antonoli, Tugba Santini, Francesco Deligianni, Xeni Garcia Alzamora, Meritxell Rutz, Erich Bieri, Oliver Brunner, Reinald Weidensteiner, Claudia Front Neurol Neurology Background: Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but additional methods for characterizing muscle composition have not thoroughly been investigated. MRI fat fraction (FF) measurement can quantify muscle fat and is often a part of standard imaging in neuromuscular dystrophies. To date, FF has been used to quantify muscle fat and assess function in CP. In this study, we aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones. Materials and Methods: A total of 9 patients (age range 8–15 years) with CP and 12 healthy controls (age range 9–16 years) were prospectively enrolled (2018–2020) after ethics committee approval. Multi-echo Dixon acquisition of the calf muscles was used for FF calculation. The images of the second echo (TE = 2.87 ms) were used for feature extraction from the soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles. The least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. RM, FF model (FFM), and combined model (CM) were built for each calf muscle. The receiver operating characteristic (ROC) curve and their respective area under the curve (AUC) values were used to evaluate model performance. Results: In total, the affected legs of 9 CP patients and the dominant legs of 12 healthy controls were analyzed. The performance of RM for soleus, gastrocnemius medialis, and gastrocnemius lateralis (AUC 0.92, 0.92, 0.82, respectively) was better than the FFM (AUC 0.88, 0.85, 0.69, respectively). The combination of both models always had a better performance than RM or FFM (AUC 0.95, 0.93, 0.83). FF was higher in the patient group (FF(S) 9.1%, FF(GM) 8.5%, and FF(GL) 10.2%) than control group (FF(S) 3.3%, FF(GM) 4.1%, FF(GL) 6.6%). Conclusion: The combination of MRI quantitative fat fraction analysis and texture analysis of muscles is a promising tool to evaluate muscle pathologies due to CP in a non-invasive manner. Frontiers Media S.A. 2021-03-22 /pmc/articles/PMC8019698/ /pubmed/33828520 http://dx.doi.org/10.3389/fneur.2021.633808 Text en Copyright © 2021 Akinci D'Antonoli, Santini, Deligianni, Garcia Alzamora, Rutz, Bieri, Brunner and Weidensteiner. 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
Akinci D'Antonoli, Tugba
Santini, Francesco
Deligianni, Xeni
Garcia Alzamora, Meritxell
Rutz, Erich
Bieri, Oliver
Brunner, Reinald
Weidensteiner, Claudia
Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy
title Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy
title_full Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy
title_fullStr Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy
title_full_unstemmed Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy
title_short Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy
title_sort combination of quantitative mri fat fraction and texture analysis to evaluate spastic muscles of children with cerebral palsy
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8019698/
https://www.ncbi.nlm.nih.gov/pubmed/33828520
http://dx.doi.org/10.3389/fneur.2021.633808
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