ArticleFrontiers in endocrinology2022
Application of MR Imaging Features in Differentiation of Renal Changes in Patients With Stage III Type 2 Diabetic Nephropathy and Normal Subjects.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 13 citations in OpenAlex.
- Identification and validation of a chronic kidney disease prediction model after radical nephrectomy: a multicenter cohort retrospective study.Annals of medicine · 2026Article
- Texture Analysis of Multiparametric Kidney MRI-A Non-Invasive Approach to Chronic Kidney Disease State.Biomedicines · 2026Article
- Kidney MRI Texture Analysis-A Universal Assessment of Kidney State and Function?Journal of clinical medicine · 2026Article
- The diagnostic value of radiomics based on two-dimensional ultrasound in staging diabetic nephropathy.BMC medical imaging · 2026Article
- Insight into Kidney Function and Microstructure Through Renal MRI-Review of the Literature.Bioengineering (Basel, Switzerland) · 2026Review
- Can radiomics-based innovations improve the diagnosis of kidney fibrosis in diabetic nephropathy?Clinical kidney journal · 2026Review
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- The role of texture analysis of T1 weighted images in diagnosis of chronic kidney disease.Scientific reports · 2026Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy.Pediatric nephrology (Berlin, Germany) · 2024Article
- MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.Insights into imaging · 2023Article
- Application of MR Imaging Characteristics in the Differentiation of Renal Changes Between Patients with Stage III Type 2 Diabetic Kidney Disease and Healthy People.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study.Frontiers in endocrinology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The objective of the study was 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 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) <30 mg/g, estimated glomerular filtration rate (eGFR) in the range of 60-120 ml/(min 1.73 m Results: There was a significant difference in sex and body mass index (BMI) (P <0.05) in the primary cohort, with no significant difference in age. In the final results, the wavelet and Laplacian-Gaussian filtering are used to extract 1,892 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, 1,638 imaging extracts features from the original T2WI image.1 first-order feature and 5 texture features were selected. A total of 1,241 imaging features were extracted from the original ADC images, and 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. The 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 sensitivity of at least 92%. Conclusion: In consequence, the texture features based on MR have broad application prospects in the early detection of DN as a relatively simple and noninvasive tool without contrast media administration.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.