A quantitative alternative to the Goutallier classification system using Lava Flex and Ideal MRI techniques: volumetric intramuscular fatty infiltration of the supraspinatus muscle, a cadaveric study.


Journal

Magma (New York, N.Y.)
ISSN: 1352-8661
Titre abrégé: MAGMA
Pays: Germany
ID NLM: 9310752

Informations de publication

Date de publication:
Dec 2019
Historique:
received: 17 05 2019
accepted: 24 08 2019
revised: 05 08 2019
pubmed: 6 9 2019
medline: 10 4 2020
entrez: 6 9 2019
Statut: ppublish

Résumé

The Goutallier classification system is the most commonly used method for grading intramuscular fatty infiltration in rotator cuff tears. This grading system presents low inter-observer reliability and an inability to provide quantitative and repeatable outcomes for intramuscular fat. We determined the correlation and reliability of two methods, the Lava Flex and Ideal IQ MRI techniques, in quantifying volumetric intramuscular fat, while also comparing to the Goutallier method. The supraspinatus muscles of seventeen cadaveric shoulders were scanned using the Lava Flex and Ideal IQ MRI imaging protocols. Histological analysis was performed on the same muscles. Agreement, reliability, and correlation analyses were performed to compare all outcomes. The Lava Flex protocol took an average of ~ 4 min, while the Ideal IQ required about ~ 11 min to complete. Bland-Altman analysis showed good agreement between the Lava Flex and Ideal IQ [LOA (- 0.10 and 0.05)], and ICC analyses showed excellent reliability (ICC (1,1) 0.948; ICC (2,1) 0.947). There was a 91% correlation between the Lava Flex and Ideal IQ MR protocols. Weighted Kappa analysis between histology and the Goutallier classification showed fair-to-moderate agreement. The Lava Flex technique, taking about 30% of the acquisition time, may prevent motion artifacts in outcomes associated with the longer Ideal IQ technique. However, potential magnetic field inhomogeneities should be considered. The Lava Flex technique may be a faster and valid alternative to the Goutallier classification system.

Identifiants

pubmed: 31486009
doi: 10.1007/s10334-019-00774-y
pii: 10.1007/s10334-019-00774-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

607-615

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Auteurs

Jose H Trevino (JH)

Department of Biomedical Engineering, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA.

Krzysztof R Gorny (KR)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Angel Gomez-Cintron (A)

Department of Radiology, The University of Texas Health Science Center, San Antonio, San Antonio, TX, USA.

Chunfeng Zhao (C)

Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA.

Hugo Giambini (H)

Department of Biomedical Engineering, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA. hugo.giambini@utsa.edu.

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