Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy.
DMD
GRMD
imaging biomarkers
machine learning
texture analysis
Journal
Muscle & nerve
ISSN: 1097-4598
Titre abrégé: Muscle Nerve
Pays: United States
ID NLM: 7803146
Informations de publication
Date de publication:
03 2019
03 2019
Historique:
received:
02
07
2018
revised:
11
11
2018
accepted:
16
11
2018
pubmed:
22
11
2018
medline:
6
7
2019
entrez:
22
11
2018
Statut:
ppublish
Résumé
Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD. To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines. A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan-time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort. The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380-386, 2019.
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
380-386Subventions
Organisme : National Science Foundation
ID : 1606136
Pays : International
Organisme : Qatar National Research Fund
ID : NPRP6-241-2-102NPRP8-1606-3-322NPRP8-293-2-124
Pays : International
Informations de copyright
© 2018 Wiley Periodicals, Inc.