Literature DB >> 30066418

Muscle imaging in laminopathies: Synthesis study identifies meaningful muscles for follow-up.

David GóMez-Andrés1, Jordi Díaz-Manera2, Aida Alejaldre2, Irene Pulido-Valdeolivas3, Laura GonzáLez-Mera4,5, Montse Olivé5, Juan José Vilchez6, Adolfo LóPez De Munain6,7, Carmen Paradas8, Nuria Muelas6, Ángel SáNchez-MontáÑez9, Alicia Alonso-Jimenez2, Marta Gómez García De la Banda10, Ivana Dabaj10, Gisèle Bonne11, Francina Munell1, Robert Y Carlier12, Susana Quijano-Roy13.   

Abstract

INTRODUCTION: Particular fibroadipose infiltration patterns have been recently described by muscle imaging in congenital and later onset forms of LMNA-related muscular dystrophies (LMNA-RD).
METHODS: Scores for fibroadipose infiltration of 23 lower limb muscles in 34 patients with LMNA-RD were collected from heat maps of 2 previous studies. Scoring systems were homogenized. Relationships between muscle infiltration and disease duration and age of onset were modeled with random forests.
RESULTS: The pattern of infiltration differs according to disease duration but not to age of disease onset. The muscles whose progression best predicts disease duration were semitendinosus, biceps femoris long head, gluteus medius, and semimembranosus. DISCUSSION: In LMNA-RD, our synthetic analysis of lower limb muscle infiltration did not find major differences between forms with different ages of onset but allowed the identification of muscles with characteristic infiltration during disease progression. Monitoring of these specific muscles by quantitative MRI may provide useful imaging biomarkers in LMNA-RD. Muscle Nerve 58:812-817, 2018.
© 2018 Wiley Periodicals, Inc.

Entities:  

Keywords:  LMNA, machine learning, magnetic resonance; biomarker; imaging; laminopathy

Mesh:

Year:  2018        PMID: 30066418     DOI: 10.1002/mus.26312

Source DB:  PubMed          Journal:  Muscle Nerve        ISSN: 0148-639X            Impact factor:   3.217


  5 in total

Review 1.  Advancements in magnetic resonance imaging-based biomarkers for muscular dystrophy.

Authors:  Doris G Leung
Journal:  Muscle Nerve       Date:  2019-05-14       Impact factor: 3.217

2.  LMNA-Related Muscular Dystrophy with Clinical Intrafamilial Variability.

Authors:  Ana Cotta; Julia F Paim; Elmano Carvalho; Jaquelin Valicek; Antonio L da Cunha Junior; Monica M Navarro; Antonio P Vargas; Maria I Lima; Camila F de Almeida; Reinaldo I Takata; Mariz Vainzof
Journal:  J Mol Neurosci       Date:  2019-08-13       Impact factor: 3.444

3.  Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury.

Authors:  Kenneth A Weber; Andrew C Smith; Marie Wasielewski; Kamran Eghtesad; Pranav A Upadhyayula; Max Wintermark; Trevor J Hastie; Todd B Parrish; Sean Mackey; James M Elliott
Journal:  Sci Rep       Date:  2019-05-28       Impact factor: 4.379

4.  Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials.

Authors:  Matteo Garibaldi; Tommaso Nicoletti; Elisabetta Bucci; Laura Fionda; Luca Leonardi; Stefania Morino; Laura Tufano; Girolamo Alfieri; Antonio Lauletta; Gioia Merlonghi; Alessia Perna; Salvatore Rossi; Enzo Ricci; Jorge Alonso Perez; Tommaso Tartaglione; Antonio Petrucci; Elena Maria Pennisi; Marco Salvetti; Gary Cutter; Jordi Díaz-Manera; Gabriella Silvestri; Giovanni Antonini
Journal:  Eur J Neurol       Date:  2021-12-06       Impact factor: 6.288

Review 5.  Is Gene-Size an Issue for the Diagnosis of Skeletal Muscle Disorders?

Authors:  Marco Savarese; Salla Välipakka; Mridul Johari; Peter Hackman; Bjarne Udd
Journal:  J Neuromuscul Dis       Date:  2020
  5 in total

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