Objective <p>To stage diabetic nephropathy (DN) using two-dimensional ultrasound (B-mode) radiomics combined with clinical features.</p> Methods <p>DN was classified into early, middle, and late stages. Two-dimensional ultrasound images and clinical biochemical data from patient records were analyzed. Radiomics features were extracted from images, and two classification scenarios were examined: early vs. middle to late DN, and early to middle vs. late DN. Lasso logistic regression was used to create nomograms integrating clinical and radiomics data. The performance of these nomograms was evaluated using ROC curves, calibration, and decision curves.</p> Results <p>242 patients with renal biopsy (early DN: n = 102; middle DN: n = 53; late DN: n = 87) were included and randomly split into training (n = 169) and validation (n = 73) sets. For early vs. middle to late DN, the nomograms achieved AUCs of 0.939 and 0.876, with sensitivities of 0.882 and 0.816, specificities of 0.896 and 0.686, and F1 scores of 0.905 and 0.775 in training and validation cohorts, respectively. For early to middle vs. late DN, AUCs were 0.951 and 0.955, with sensitivities of 0.767 and 0.889, specificities of 0.917 and 0.913, and F1 scores of 0.800 and 0.873, respectively. Decision curve analysis confirmed the superiority of the combined model.</p> Conclusion <p>Nomograms based on ultrasound radiomics and clinical features effectively distinguish DN stages non-invasively.</p> Clinical trial number <p>Not applicable.</p>