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Correlation analysis of quantitative MRI measurements of thigh muscles with histopathology in patients with idiopathic inflammatory myopathy

OBJECTIVES: To validate the correlation between histopathological findings and quantitative magnetic resonance imaging (qMRI) fat fraction (FF) and water T2 mapping in patients with idiopathic inflammatory myopathy (IIM). METHODS: The study included 13 patients with histopathologically confirmed IIM...

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Detalles Bibliográficos
Autores principales: Wang, Fengdan, Fang, Shiyuan, Li, Jia, Yuan, Ling, Hou, Bo, Zhu, Jinxia, Jiao, Yang, Liu, Zhi, Qian, Min, Santini, Francesco, Wang, Qian, Chen, Lin, Feng, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435435/
https://www.ncbi.nlm.nih.gov/pubmed/37589922
http://dx.doi.org/10.1186/s41747-023-00350-z
Descripción
Sumario:OBJECTIVES: To validate the correlation between histopathological findings and quantitative magnetic resonance imaging (qMRI) fat fraction (FF) and water T2 mapping in patients with idiopathic inflammatory myopathy (IIM). METHODS: The study included 13 patients with histopathologically confirmed IIM who underwent dedicated thigh qMRI scanning within 1 month before open muscle biopsy. For the biopsied muscles, FF derived from the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) and T2 time from T2 mapping with chemical shift selective fat saturation were measured using a machine learning software. Individual histochemical and immunohistochemical slides were evaluated using a 5-point Likert score. Inter-reader agreement and the correlation between qMRI markers and histopathological scores were analyzed. RESULTS: Readers showed good to perfect agreement in qMRI measurements and most histopathological scores. FF of the biopsied muscles was positively correlated with the amount of fat in histopathological slides (p = 0.031). Prolonged T2 time was associated with the degree of variation in myofiber size, inflammatory cell infiltration, and amount of connective tissues (p ≤ 0.008 for all). CONCLUSIONS: Using the machine learning-based muscle segmentation method, a positive correlation was confirmed between qMRI biomarkers and histopathological findings of patients with IIM. This finding provides a basis for using qMRI as a non-invasive tool in the diagnostic workflow of IIM. RELEVANCE STATEMENT: By using ML-based muscle segmentation, a correlation between qMRI biomarkers and histopathology was found in patients with IIM: qMRI is a potential non-invasive tool in this clinical setting. KEY POINTS: • Quantitative magnetic resonance imaging measurements using machine learning-based muscle segmentation have good consistency and reproductivity. • Fat fraction of idiopathic inflammatory myopathy (IIM) correlated with the amount of fat at histopathology. • Prolonged T2 time was associated with muscle inflammation in IIM. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00350-z.