Sara Nagy, Olga Kubassova, Patricia Hafner, Sabine Schädelin, Simone Schmidt, Michael Sinnreich, Jonas Schröder, Oliver Bieri, Mikael Boesen, Dirk Fischer
{"title":"Automated analysis of quantitative muscle MRI and its reliability in patients with Duchenne muscular dystrophy.","authors":"Sara Nagy, Olga Kubassova, Patricia Hafner, Sabine Schädelin, Simone Schmidt, Michael Sinnreich, Jonas Schröder, Oliver Bieri, Mikael Boesen, Dirk Fischer","doi":"10.1177/22143602251319184","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Quantitative muscle MRI is one of the most promising biomarkers to detect subclinical disease progression in patients with neuromuscular disorders, including Duchenne muscular dystrophy (DMD). However, its clinical application has been limited partly due to the time-intensive process of manual segmentation.</p><p><strong>Objective: </strong>We present a simple and fast automated approach to obtain quantitative measurement of thigh muscle fat fraction and investigate its reliability in patients with DMD.</p><p><strong>Methods: </strong>Clinical and radiological baseline and 6-month follow-up data of 41 ambulant patients with DMD were analysed retrospectively. Axial 2-point Dixon MR images of all thigh muscles were used to quantify mean fat fraction, while clinical outcomes were measured by the Motor Function Measure (MFM) and its D1 domain. Data obtained by automated segmentation were compared to manual segmentation and correlated with clinical outcomes. Results were also used to compare the statistical power when using automated or manual segmentation.</p><p><strong>Results: </strong>A mean increase of 3.55% in thigh muscle fat fraction at 6-month follow-up could be detected by both methods without any significant difference between them (p=0.437). The automated muscle segmentation method demonstrated a strong correlation with manually segmented data (Pearson's ρ = 0.97). Additionally, there was no statistically significant difference between the automated and manual segmentation methods in their association with clinical progression, as measured by the total MFM score and its D1 domain (p = 0.235 and p = 0.425, respectively).</p><p><strong>Conclusions: </strong>The presented automated segmentation technique is a fast and reliable tool for assessing disease progression, particularly in the early stages of DMD. It is one of the few studies validated using manual segmentation, and with further refinement, it has the potential to become a good surrogate marker for disease progression in various neuromuscular disorders.</p>","PeriodicalId":16536,"journal":{"name":"Journal of neuromuscular diseases","volume":" ","pages":"22143602251319184"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neuromuscular diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/22143602251319184","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Quantitative muscle MRI is one of the most promising biomarkers to detect subclinical disease progression in patients with neuromuscular disorders, including Duchenne muscular dystrophy (DMD). However, its clinical application has been limited partly due to the time-intensive process of manual segmentation.
Objective: We present a simple and fast automated approach to obtain quantitative measurement of thigh muscle fat fraction and investigate its reliability in patients with DMD.
Methods: Clinical and radiological baseline and 6-month follow-up data of 41 ambulant patients with DMD were analysed retrospectively. Axial 2-point Dixon MR images of all thigh muscles were used to quantify mean fat fraction, while clinical outcomes were measured by the Motor Function Measure (MFM) and its D1 domain. Data obtained by automated segmentation were compared to manual segmentation and correlated with clinical outcomes. Results were also used to compare the statistical power when using automated or manual segmentation.
Results: A mean increase of 3.55% in thigh muscle fat fraction at 6-month follow-up could be detected by both methods without any significant difference between them (p=0.437). The automated muscle segmentation method demonstrated a strong correlation with manually segmented data (Pearson's ρ = 0.97). Additionally, there was no statistically significant difference between the automated and manual segmentation methods in their association with clinical progression, as measured by the total MFM score and its D1 domain (p = 0.235 and p = 0.425, respectively).
Conclusions: The presented automated segmentation technique is a fast and reliable tool for assessing disease progression, particularly in the early stages of DMD. It is one of the few studies validated using manual segmentation, and with further refinement, it has the potential to become a good surrogate marker for disease progression in various neuromuscular disorders.
期刊介绍：
The Journal of Neuromuscular Diseases aims to facilitate progress in understanding the molecular genetics/correlates, pathogenesis, pharmacology, diagnosis and treatment of acquired and genetic neuromuscular diseases (including muscular dystrophy, myasthenia gravis, spinal muscular atrophy, neuropathies, myopathies, myotonias and myositis). The journal publishes research reports, reviews, short communications, letters-to-the-editor, and will consider research that has negative findings. The journal is dedicated to providing an open forum for original research in basic science, translational and clinical research that will improve our fundamental understanding and lead to effective treatments of neuromuscular diseases.
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