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Nephrology

Two-Dimensional Ultrasound-Based Radiomics Nomogram for Diabetic Kidney Disease: A Pilot Study

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Pages 1877-1885 | Received 04 Feb 2024, Accepted 21 Apr 2024, Published online: 06 May 2024

Figures & data

Figure 1 The workflow of the critical steps in constructing an ultrasound imaging-based rad-score for a patient with Type 2 diabetes.

Figure 1 The workflow of the critical steps in constructing an ultrasound imaging-based rad-score for a patient with Type 2 diabetes.

Table 1 The Baseline Characteristics of All Patients

Figure 2 Radiomics feature selection using LASSO and SVM-RFE regression for establishing the rad-score. (A) LASSO coefficient distribution of all the radiomics features. (B) SVM-RFE regression for all the features. (C) The overlapping of features of the SVM-RFE and LASSO regression. (D) The weights of the selected radiomics features.

Figure 2 Radiomics feature selection using LASSO and SVM-RFE regression for establishing the rad-score. (A) LASSO coefficient distribution of all the radiomics features. (B) SVM-RFE regression for all the features. (C) The overlapping of features of the SVM-RFE and LASSO regression. (D) The weights of the selected radiomics features.

Figure 3 (A) The radiomics nomogram for the risk of DKD in diabetic patients. The receiver operating characteristic curve (B), the calibration curve (C), and the decision curve analysis (D) of the radiomics nomogram for DKD.

Figure 3 (A) The radiomics nomogram for the risk of DKD in diabetic patients. The receiver operating characteristic curve (B), the calibration curve (C), and the decision curve analysis (D) of the radiomics nomogram for DKD.

Table 2 Univariate and Multivariate Logistic Regression Analysis for DKD