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

组织病理学 磁共振成像 医学 肌肉活检 活检 炎性肌病 病理 相关性 肌病 放射科 数学 几何学
作者
Fengdan Wang,Shih Chieh Fang,Jia Li,Ling Yuan,Bo Hou,Jinxia Zhu,Yang Jiao,Zhi Li,Min Qian,Francesco Santini,Qian Wang,Lin Chen,Feng Feng
出处
期刊:European Radiology Experimental [Springer Nature]
卷期号:7 (1) 被引量:3
标识
DOI:10.1186/s41747-023-00350-z
摘要

Abstract 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
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