3D Radiomic Texture Analysis of Quantitative Muscle MRI Enhances the Distinction Between Myotonic Dystrophy Type 1 and Charcot–Marie-Tooth Neuropathy Type 1A: A Proof-of-Concept Study
Louise Iterbeke, Lotte Huysmans, Kobe Bamps, Ronald Peeters, Veerle Goosens, Frederik Maes, Patrick Dupont, Kristl G. Claeys
{"title":"3D Radiomic Texture Analysis of Quantitative Muscle MRI Enhances the Distinction Between Myotonic Dystrophy Type 1 and Charcot–Marie-Tooth Neuropathy Type 1A: A Proof-of-Concept Study","authors":"Louise Iterbeke, Lotte Huysmans, Kobe Bamps, Ronald Peeters, Veerle Goosens, Frederik Maes, Patrick Dupont, Kristl G. Claeys","doi":"10.1111/ene.70719","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Differentiating myogenic from neurogenic neuromuscular diseases (NMDs) can be clinically challenging. While quantitative muscle MRI (qMRI) with proton density fat fraction (PDFF,%) quantifies fat replacement, it misses micro-spatial patterns linked to underlying pathology. This study investigates whether qMRI with 3D radiomic texture analysis (TA) might improve differentiation between myogenic and neurogenic diseases, using myotonic dystrophy type 1 (DM1) and Charcot–Marie-Tooth neuropathy type 1A (CMT1A) as proof-of-concept models.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Thirty-three adults with DM1, 33 with CMT1A, and 33 matched healthy controls were included. qMRI on a 3T Philips Achieva system using a 6-point Dixon sequence generated PDFF(%) maps of the lower limbs, and a convolutional neural network performed 3D segmentation of 28 lower limb muscles. We extracted macroscopic features, including muscle volume, asymmetry, and disto-proximal gradients, alongside micro-spatial radiomic features (entropy, contrast, homogeneity) to quantify tissue heterogeneity.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Both patient cohorts exhibited higher PDFF(%) in all lower limb muscles compared to controls (<i>p</i> < 0.001). DM1 predominantly involved the posterior compartment, while CMT1A targeted the anterolateral compartment with significantly steeper disto-proximal fat gradients (<i>p</i> < 0.05). TA revealed higher entropy and contrast, and lower homogeneity in CMT1A compared to DM1, reflecting a more reticular pattern of fat infiltration vs. the confluent pattern in DM1.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>In this proof-of-concept study, 3D radiomic texture analysis of PDFF(%) maps revealed distinct spatial patterns of fat replacement in DM1 and CMT1A. Integrating radiomic and conventional qMRI features may enhance the non-invasive distinction between DM1 and CMT1A and warrants further investigation in a broader range of NMDs.</p>\n </section>\n </div>","PeriodicalId":11954,"journal":{"name":"European Journal of Neurology","volume":"33 8","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ene.70719","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Neurology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ene.70719","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Differentiating myogenic from neurogenic neuromuscular diseases (NMDs) can be clinically challenging. While quantitative muscle MRI (qMRI) with proton density fat fraction (PDFF,%) quantifies fat replacement, it misses micro-spatial patterns linked to underlying pathology. This study investigates whether qMRI with 3D radiomic texture analysis (TA) might improve differentiation between myogenic and neurogenic diseases, using myotonic dystrophy type 1 (DM1) and Charcot–Marie-Tooth neuropathy type 1A (CMT1A) as proof-of-concept models.
Methods
Thirty-three adults with DM1, 33 with CMT1A, and 33 matched healthy controls were included. qMRI on a 3T Philips Achieva system using a 6-point Dixon sequence generated PDFF(%) maps of the lower limbs, and a convolutional neural network performed 3D segmentation of 28 lower limb muscles. We extracted macroscopic features, including muscle volume, asymmetry, and disto-proximal gradients, alongside micro-spatial radiomic features (entropy, contrast, homogeneity) to quantify tissue heterogeneity.
Results
Both patient cohorts exhibited higher PDFF(%) in all lower limb muscles compared to controls (p < 0.001). DM1 predominantly involved the posterior compartment, while CMT1A targeted the anterolateral compartment with significantly steeper disto-proximal fat gradients (p < 0.05). TA revealed higher entropy and contrast, and lower homogeneity in CMT1A compared to DM1, reflecting a more reticular pattern of fat infiltration vs. the confluent pattern in DM1.
Conclusion
In this proof-of-concept study, 3D radiomic texture analysis of PDFF(%) maps revealed distinct spatial patterns of fat replacement in DM1 and CMT1A. Integrating radiomic and conventional qMRI features may enhance the non-invasive distinction between DM1 and CMT1A and warrants further investigation in a broader range of NMDs.
期刊介绍:
The European Journal of Neurology is the official journal of the European Academy of Neurology and covers all areas of clinical and basic research in neurology, including pre-clinical research of immediate translational value for new potential treatments. Emphasis is placed on major diseases of large clinical and socio-economic importance (dementia, stroke, epilepsy, headache, multiple sclerosis, movement disorders, and infectious diseases).