Abstract
Objective: Utilizing ultrasound radiomics, we developed a machine learning (ML) model to construct a nomogram for the non-invasive evaluation of glomerular status in diabetic kidney disease (DKD).
Materials and Methods: Patients with DKD who underwent renal biopsy were retrospectively enrolled between February 2017 and February 2023. The patients were classified into mild or moderate-severe glomerular severity based on pathological findings. All patients were randomly divided into a training (n = 79) or testing cohort (n = 35). Radiomics features were extracted from ultrasound images, and a logistic regression ML algorithm was applied to construct an ultrasound radiomic model after selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm (LASSO). A clinical model was created following univariate and multivariate logistic regression analyses of the patient's clinical characteristics. Then, the clinical-radiomics model was constructed by combining rad scores and independent clinical characteristics and plotting the nomogram. The receiver operating characteristic curve (ROC) and decision curve analysis (DCA), respectively, were used to evaluate the prediction abilities of the clinical model, ultrasound-radiomics model, and clinical-radiomics model.
Results: A total of 114 DKD patients were included in the study, including 43 with mild glomerulopathy and 71 with moderate-severe glomerulopathy. The area under the curve (AUC) for the clinical model based on clinical features and the radiomic model based on 2D ultrasound images in the testing cohort was 0.729 and 0.761, respectively. Further, the AUC for the clinical-radiomics nomogram was constructed by combining clinical features, and the rad score was 0.850 in the testing cohort. The outcomes were better than those of both the radiomic and clinical single-model approaches.
Conclusion: The nomogram constructed by combining ultrasound radiomics and clinical features has good performance in assessing the glomerular status of patients with DKD and will help clinicians monitor the progression of DKD.
