Abstract
BACKGROUND: The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy. METHODS: A total of 623 renal ultrasound images from 46 membranous nephropathy (MN) and 22 IgA nephropathy patients were collected. The cases and images were divided into a training group (51 cases with 470 images) and a test group (17 cases with 153 images). A total of 180 dimensional features were designed and extracted from the renal parenchyma in the ultrasound images. Least absolute shrinkage and selection operator (LASSO) logistic regression was then applied to these normalized radiomics features to select the features with the highest correlations. Four machine learning classifiers, including logistic regression, a support vector machine (SVM), a random forest, and a K-nearest neighbour classifier, were deployed for the classification of MN and IgA nephropathy. Subsequently, the results were assessed according to accuracy and receiver operating characteristic (ROC) curves. RESULTS: Patients with MN were older than patients with IgA nephropathy. MN primarily manifested in patients as nephrotic syndrome, whereas IgA nephropathy presented mainly as nephritic syndrome. Analysis of the classification performance of the four classifiers for IgA nephropathy and MN revealed that the random forest achieved the highest area under the ROC curve (AUC) (0.7639) and the highest specificity (0.8750). However, logistic regression attained the highest accuracy (0.7647) and the highest sensitivity (0.8889). CONCLUSIONS: Quantitative radiomics imaging features extracted from digital renal ultrasound are fully capable of distinguishing IgA nephropathy from MN. Radiomics analysis, a non-invasive method, is helpful for histological classification of glomerulopathy.