Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy.
DMD
GRMD
imaging biomarkers
machine learning
muscle percentage index
texture
Journal
Muscle & nerve
ISSN: 1097-4598
Titre abrégé: Muscle Nerve
Pays: United States
ID NLM: 7803146
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
30
08
2018
revised:
05
08
2019
accepted:
06
08
2019
pubmed:
10
8
2019
medline:
14
1
2020
entrez:
10
8
2019
Statut:
ppublish
Résumé
Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD. To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients). The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs. MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
621-628Informations de copyright
© 2019 Wiley Periodicals, Inc.
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