Dual-modality ultrasound radiomics model for classifying diabetic peripheral neuropathy in type 2 diabetes: a multicenter prospective study

Diabetic peripheral neuropathy (DPN) is a prevalent and disabling complication of type 2 diabetes mellitus (T2DM), yet detection remains constrained by limited accessibility of nerve conduction studies. This study developed and validated a dual-modality ultrasound radiomics model for individualized DPN classification. The model was trained on data from 253 patients with T2DM, who were enrolled in three centers between June 2025 and February 2026. The patients were allocated to training (n = 122), internal test (n = 53), and external validation (n = 78) cohorts. Radiomics features were extracted from longitudinal B-mode ultrasound and shear wave elastography images of the tibial nerve. After reproducibility filtering, batch-effect correction, and elastic-net feature selection, four machine learning algorithms were compared and the best-performing was used to construct modality-specific radiomics scores (Rad-scores). A combined model integrating Rad-scores with independently associated clinical factors was developed. The model demonstrated good discrimination across all cohorts, outperforming other models. Decision curve analysis confirmed clinical benefit. SHapley Additive exPlanations (SHAP) analysis identified diabetes duration as the most influential variable, followed by the shear wave elastography Rad-score. The combined model holds promise as a noninvasive complement to nerve conduction studies for individualized DPN risk stratification.