Application of MR Imaging Features in Differentiation of Renal Changes in Patients With Stage III Type 2 Diabetic Nephropathy and Normal Subjects - Yu, Baoting; Huang, Chencui; Fan, Xiaofei; Li, Feng; Zhang, Jianzhong; Song, Zihan; Zhi, Nan; Ding, Jun (Frontiers in Endocrinology, 2022)
Objective To explore the value of MRI texture features based on T1WI, T2-FS and diffusion-weighted imaging (DWI) in differentiation of renal changes in patients with stage III type 2 of diabetic nephropathy (DN) and normal subjects. Materials and methods A retrospective analysis was performed to analyze 44 healthy volunteers (group A) and 40 patients with stage III type 2 diabetic nephropathy (group B) with microalbuminuria. Urinary albumin to creatinine ratio (ACR) < 30mg/g, estimated glomerular filtration rate (eGFR) in the range of 60-120ml/(min1.73m2), and randomly divided into primary cohort and test cohort. Conventional MRI and DWI of kidney were performed using 1.5 T magnetic resonance imaging (MRI). The outline of the renal parenchyma were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method.Results There was a significant difference in sex and body mass index (BMI) (P <0.05) in the primary cohort, no significant difference in age. In the final results, the wavelet and Laplacian-Gaussian filtering are used to extract 1892 image features from the original T1WI image, and the LASSO algorithm is used for selection. One first-order feature and six texture features are selected through 10 cross-validations. In the mass, 1638 imaging extracts features from the original T2WI image.1 first-order feature and 5 texture features were selected. A total of 1241 imaging features were extracted from the original ADC images, 5 texture features were selected. Using LASSO-Logistic regression analysis, 10 features were selected for modeling, and a combined diagnosis model of diabetic nephropathy based on texture features was established. Average unit cost in the logistic regression model was 0.98, the 95% confidence interval for the predictive efficacy was 0.9486-1.0, specificity 0.97 and precision 0.93, particularly. ROC curves also revealed that the model could distinguish with high sensitive of at least 92%. Conclusion In consequence, the texture features based on MR have broad application prospects in early detection. DN as a relatively simple and noninvasive tool without contrast media administration.