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Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis

BACKGROUND: The diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mostly a subjective process and difficult to quantify. We have tested if network science can provide a novel framework to extract use...

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Autores principales: Sáez, Aurora, Rivas, Eloy, Montero-Sánchez, Adoración, Paradas, Carmen, Acha, Begoña, Pascual, Alberto, Serrano, Carmen, Escudero, Luis M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621542/
https://www.ncbi.nlm.nih.gov/pubmed/23514382
http://dx.doi.org/10.1186/1741-7015-11-77
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author Sáez, Aurora
Rivas, Eloy
Montero-Sánchez, Adoración
Paradas, Carmen
Acha, Begoña
Pascual, Alberto
Serrano, Carmen
Escudero, Luis M
author_facet Sáez, Aurora
Rivas, Eloy
Montero-Sánchez, Adoración
Paradas, Carmen
Acha, Begoña
Pascual, Alberto
Serrano, Carmen
Escudero, Luis M
author_sort Sáez, Aurora
collection PubMed
description BACKGROUND: The diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mostly a subjective process and difficult to quantify. We have tested if network science can provide a novel framework to extract useful information from muscle biopsies, developing a novel method that analyzes muscle samples in an objective, automated, fast and precise manner. METHODS: Our database consisted of 102 muscle biopsy images from 70 individuals (including controls, patients with neurogenic atrophies and patients with muscular dystrophies). We used this to develop a new method, Neuromuscular DIseases Computerized Image Analysis (NDICIA), that uses network science analysis to capture the defining signature of muscle biopsy images. NDICIA characterizes muscle tissues by representing each image as a network, with fibers serving as nodes and fiber contacts as links. RESULTS: After a ‘training’ phase with control and pathological biopsies, NDICIA was able to quantify the degree of pathology of each sample. We validated our method by comparing NDICIA quantification of the severity of muscular dystrophies with a pathologist’s evaluation of the degree of pathology, resulting in a strong correlation (R = 0.900, P <0.00001). Importantly, our approach can be used to quantify new images without the need for prior ‘training’. Therefore, we show that network science analysis captures the useful information contained in muscle biopsies, helping the diagnosis of muscular dystrophies and neurogenic atrophies. CONCLUSIONS: Our novel network analysis approach will serve as a valuable tool for assessing the etiology of muscular dystrophies or neurogenic atrophies, and has the potential to quantify treatment outcomes in preclinical and clinical trials.
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spelling pubmed-36215422013-04-15 Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis Sáez, Aurora Rivas, Eloy Montero-Sánchez, Adoración Paradas, Carmen Acha, Begoña Pascual, Alberto Serrano, Carmen Escudero, Luis M BMC Med Technical Advance BACKGROUND: The diagnosis of neuromuscular diseases is strongly based on the histological characterization of muscle biopsies. However, this morphological analysis is mostly a subjective process and difficult to quantify. We have tested if network science can provide a novel framework to extract useful information from muscle biopsies, developing a novel method that analyzes muscle samples in an objective, automated, fast and precise manner. METHODS: Our database consisted of 102 muscle biopsy images from 70 individuals (including controls, patients with neurogenic atrophies and patients with muscular dystrophies). We used this to develop a new method, Neuromuscular DIseases Computerized Image Analysis (NDICIA), that uses network science analysis to capture the defining signature of muscle biopsy images. NDICIA characterizes muscle tissues by representing each image as a network, with fibers serving as nodes and fiber contacts as links. RESULTS: After a ‘training’ phase with control and pathological biopsies, NDICIA was able to quantify the degree of pathology of each sample. We validated our method by comparing NDICIA quantification of the severity of muscular dystrophies with a pathologist’s evaluation of the degree of pathology, resulting in a strong correlation (R = 0.900, P <0.00001). Importantly, our approach can be used to quantify new images without the need for prior ‘training’. Therefore, we show that network science analysis captures the useful information contained in muscle biopsies, helping the diagnosis of muscular dystrophies and neurogenic atrophies. CONCLUSIONS: Our novel network analysis approach will serve as a valuable tool for assessing the etiology of muscular dystrophies or neurogenic atrophies, and has the potential to quantify treatment outcomes in preclinical and clinical trials. BioMed Central 2013-03-20 /pmc/articles/PMC3621542/ /pubmed/23514382 http://dx.doi.org/10.1186/1741-7015-11-77 Text en Copyright © 2013 Sáez et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Advance
Sáez, Aurora
Rivas, Eloy
Montero-Sánchez, Adoración
Paradas, Carmen
Acha, Begoña
Pascual, Alberto
Serrano, Carmen
Escudero, Luis M
Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
title Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
title_full Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
title_fullStr Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
title_full_unstemmed Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
title_short Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
title_sort quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621542/
https://www.ncbi.nlm.nih.gov/pubmed/23514382
http://dx.doi.org/10.1186/1741-7015-11-77
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