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

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.

In total, 253 T2DM patients from three centers were prospectively enrolled between June 2025 and February 2026, 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. Discrimination, calibration, clinical usefulness, and SHapley Additive exPlanations (SHAP) interpretability were evaluated.

Multivariable analysis identified diabetes duration and minimum elastic modulus as independently associated factors, with both Rad-scores significantly associated with DPN status. The combined model demonstrated good discrimination across all cohorts (AUC 0.892 [95% CI 0.828-0.950], 0.824 [0.696-0.925], and 0.838 [0.735-0.926]), outperforming all other models (p < 0.05), with adequate calibration in both training and internal test cohorts (p > 0.05). Decision curve analysis confirmed clinical benefit. SHAP analysis identified diabetes duration as the most influential variable, followed by the shear wave elastography Rad-score.

The combined model demonstrates favorable diagnostic performance for individualized DPN risk stratification and holds promise as a noninvasive complement to nerve conduction.