| Sumario: | Background Machine learning applied to computed tomography (CT) images captures variations in skeletal muscle texture and structure not detectable by conventional measures. These novel 'radiomic' features may offer added value in predicting muscle function and physical performance beyond traditional CT-derived muscle area and density. We aimed to identify radiomic features of skeletal muscle associated with grip strength, leg power and walking speed in older men. Methods In the Osteoporotic Fractures in Men study (n = 3404; 73.8 ± 5.9 years), participants underwent baseline CT scans (trunk L1, L3; right and left thigh) and assessments of grip strength, 6 m walk and leg power (Nottingham Power Rig). Muscle area and density were derived from automatically segmented CT images. Radiomic features were extracted using PyRadiomics. Elastic net regression and factor analysis identified key radiomic features; associations with muscle function/performance were assessed using regression models. Results Factor analysis identified nine factors for Trunk-L1 and eight for the other regions. Trunk-based factors significantly improved model fit for leg power, grip strength and walking speed (P < .05). Factor 1, representing body size and muscle texture complexity, was the most consistent predictor across outcomes. The Gray-Level Co-occurrence Matrix feature 'cluster prominence' was inversely associated with walking speed (β = −0.06 at L1; −0.05 at L3) and leg power (β = −0.05 at L1), independent of age, height, weight, muscle CSA, muscle density and technical group. Conclusion CT-derived radiomic features in the trunk region may reflect skeletal muscle structural characteristics that independently relate to strength, power and mobility in older men.
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