ArticleFrontiers in endocrinology2026
CT-based renal and body-composition radiomics model to improve the detection ability of diabetic kidney disease in patients with type 2 diabetes mellitus.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: 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). Methods: 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. Results: 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. Conclusion: CT-based renal and perirenal radiomics features demonstrate strong efficacy in identifying DKD in patients with T2DM.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.