Authors
Louise Iterbeke, Lotte Huysmans, Kobe Bamps, Ronald Peeters, Veerle Goosens, Frederik Maes, Patrick Dupont, Kristl G Claeys
Published in
European journal of neurology. Volume 33. Issue 8. Pages e70719.
Abstract
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.
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.
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.
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.
PMID:
42538854
Bibliographic data and abstract were imported from PubMed on 01 Aug 2026.
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