Ultrasound radiomics-based machine learning models for differentiating diabetic kidney disease from non-diabetic kidney disease in type 2 diabetes.

BACKGROUND AND

objectiveNoninvasive differentiation of diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD) in patients with type 2 diabetes mellitus (T2DM) remains challenging, as conventional clinical markers and ultrasound imaging lack sufficient accuracy and reliability. This limitation often leads to unnecessary renal biopsies or delayed diagnosis. To address this gap, this study aimed to develop and validate a machine learning model integrating renal ultrasound radiomic features and clinical predictors to distinguish DKD from NDKD in T2DM patients.

methodsPatients with T2DM who underwent renal biopsy were retrospectively enrolled from three centers. Based on biopsy findings, patients were classified as DKD or NDKD, with overlapping lesions assigned to DKD. Renal ultrasound images were collected for ROI delineation and radiomic feature extraction. Clinical predictors were selected using logistic regression, correlation analysis, and LASSO. SVM, KNN, RF, and XGBoost models were developed using radiomic features alone or combined with clinical variables. Performance was evaluated by AUC, accuracy, precision, recall, and F1-score, and SHAP was used for model interpretation.

resultsUnivariate analysis identified serum creatinine, eGFR, albumin and LDL‑C as significant variables, and eGFR was selected as the final clinical predictor after LASSO and multivariable logistic regression. The radiomics‑only model performed well in the training cohort but showed lower performance in the validation cohorts. The integrated model combining radiomic features with eGFR achieved AUCs of 0.991, 0.895 and 0.721 in the training, internal validation and external validation cohorts, respectively, with corresponding F1‑scores of 0.939, 0.815 and 0.714. DeLong's test further showed that XGBoost performed better than several comparator algorithms in the training and internal validation cohorts, whereas no significant differences were observed among models in the external validation cohort.

conclusionThe XGBoost model integrating renal ultrasound radiomic features with eGFR showed favourable performance for differentiating DKD from NDKD in patients with T2DM. By using routinely available ultrasound images and a readily accessible clinical indicator, this model may serve as a non-invasive auxiliary tool for preliminary screening in primary-level hospitals or nephrology departments, providing supportive evidence for individualized assessment and renal biopsy decision-making.