High-Frequency Ultrasound Radiomics Combined with Clinical Features for Detecting OMERACT-Defined Metacarpophalangeal Joint Cartilage Damage in Early Rheumatoid Arthritis

Background/Objectives: The aim of this study was to develop and validate a high-frequency ultrasound radiomics-based model for quantitative assessment of metacarpophalangeal (MCP) joint cartilage damage in early rheumatoid arthritis (RA). Methods: 656 MCP joints from 99 early RA patients and 65 healthy controls were prospectively enrolled and graded according to the Outcome Measures in Rheumatology (OMERACT) system. After radiomics feature extraction, five machine learning classifiers were evaluated. Radiomics, clinical, and combined models were constructed and assessed. Radiomics scores were compared among healthy grade 0 joints, early RA grade 0 joints stratified into two risk subgroups, and RA grade ≥ 1 joints. SHapley Additive exPlanations (SHAP) analysis was used for interpretation. Results: Eight stable radiomics features were selected. Among classifiers, support vector machine achieved the highest cross-validated performance and was selected as the final radiomics classifier (validation AUC = 0.804). The combined model, integrating radiomics features with age, disease duration, and Disease Activity Score in 28 joints, achieved the best diagnostic performance (AUC = 0.855), significantly outperforming both the radiomics and clinical models. Among OMERACT grade 0 joints, the high-risk subgroup demonstrated elevated radiomics-derived scores. SHAP analysis identified original_shape2D_PerimeterSurfaceRatio as the strongest contributor. Conclusions: High-frequency ultrasound radiomics combined with clinical features demonstrated strong performance in detecting MCP joint cartilage damage in early RA and may provide a quantitative extension to conventional semiquantitative assessment.