CT-based renal and body-composition radiomics model to improve the detection ability of diabetic kidney disease in patients with type 2 diabetes mellitus.

Authors

Baoli Hao, Honghao Sun, Zimeng Yang, Jinlei Fan, Liping Zuo, Peng Du, Cheng Li, Wangshu Cai, Jiqing Li, Peng Zhou, Guoqiang Tian, Dexin Yu

Published in

Frontiers in endocrinology. Volume 17. Pages 1904758. Epub Aug 03, 2026.

Abstract

Conventional imaging modalities exhibit limited capability in distinguishing diabetic kidney disease (DKD), but radiomics could expand image-derived information. Kidney, perirenal tissue and body composition, whether considered in macroscopic or microscopic profile, are associated with kidney function. The present study seeks to evaluate the efficacy of computed tomography (CT)-derived radiomic features of the kidney and body composition in detecting DKD among individuals with type 2 diabetes mellitus (T2DM).
Patients with T2DM who underwent abdominal CT and renal function test at two institutions were enrolled. Participants from one institution (n = 256) were randomly allocated into training and internal validation cohorts, whereas individuals from another institution constituted the external validation cohort (n = 64). Two- and three-dimensional radiomics features were retrieved from kidneys, perirenal and renal sinus fat, visceral and subcutaneous fat, and skeletal muscles. Radiomics, clinical, and integrated models were developed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis.
The radiomics model achieved impressive AUCs of 0.869 (95% CI, 0.809-0.929), 0.831 (95% CI, 0.721-0.940), and 0.833 (95% CI, 0.687-0.979) in the training cohort, internal and external validation cohorts, respectively. The combined model outperformed the other two models, achieving AUCs (95% CI) of 0.913 (0.868-0.957), 0.919 (0.849-0.989), and 0.867 (0.762-0.973) in the training, internal validation, and external validation cohorts, respectively.
CT-based renal and perirenal radiomics features demonstrate strong efficacy in identifying DKD in patients with T2DM.

PMID:
42609275
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.