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.