Diabetes, Metabolic Syndrome and Obesity (Jul 2023)

Application of MR Imaging Characteristics in the Differentiation of Renal Changes Between Patients with Stage III Type 2 Diabetic Kidney Disease and Healthy People

  • Zhang H,
  • Yu B,
  • Yang H,
  • Ying H,
  • Qu X,
  • Zhu L,
  • Wang C,
  • Ding J

Journal volume & issue
Vol. Volume 16
pp. 2177 – 2186

Abstract

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Hao Zhang,* Baoting Yu,* Hongsheng Yang, Hongfei Ying, Xiaolong Qu, Lilan Zhu, Cong Wang, Jun Ding Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, 130021, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jun Ding, Department of Radiology, China-Japan Union Hospital of Jilin University, No. 829 of Xinmin Street, Chaoyang District, Changchun, 130021, People’s Republic of China, Tel/Fax +86 04 70 8499 7637, Email [email protected]: To explore the value of 1.5T magnetic resonance (MR) fat saturation-T2-weighted imaging (FS-T2WI) and apparent diffusion coefficient (ADC) imaging texture features in distinguishing the renal changes of patients with stage III type 2 diabetic kidney disease (DKD) from healthy people.Methods: This study collected 55 patients with stage III DKD (39 males and 16 females) and 33 healthy controls (13 males and 20 females) from December 2021 to June 2022 in the China-Japan Union Hospital of Jilin University. All subjects were randomly divided in a ratio of 6:4 to extract and screen the FS-T2WI and ADC texture features of the right kidney of the subjects. The area under the curve (AUC) was used to assess the diagnostic accuracy of each model.Results: There were significant differences between urea, creatinine and sex (p< 0.05) of the two groups in the training and test set, and no significant difference in age and body mass index (BMI). We extracted 1409 imaging features from the original ADC sequence and selected them by wavelet and Laplace-Gaussian filter and LASSO algorithm, and using the same methods of FS-T2WI. Finally, FS-T2WI and ADC models were selected to construct the united model, including 3 first-order features and 8 texture features. The AUC values of the training set of FS-T2WI, ADC, FS-T2WI+ADC combined logistic regression model were 0.96, 0.91, 0.98; the AUC values of the test set were 0.91, 0.89 and 0.93, and the specificity and accuracy values of the united model were 0.90 and 0.89, respectively.Conclusion: FS-T2WI and ADC imaging features based on 1.5 T MR had diagnostic value in the early diagnosis of DKD stage III, and the combined model of FS-T2WI and ADC had high diagnostic efficiency.Keywords: diabetic kidney disease, magnetic resonance imaging, radiomics

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