ArticleJournal of clinical medicine2022
Radiomics-Based Image Phenotyping of Kidney Apparent Diffusion Coefficient Maps: Preliminary Feasibility & Efficacy.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis.Renal failure · 2024Pooled it
- Diffusion MRI Radiomics Predict Renal Function Decline in ADPKD.Kidney international reports · 2026Article
- Quantitative MRI of pediatric chronic kidney disease and renal fibrosis: potential non-invasive clinical biomarkers.Pediatric radiology · 2026Review
- Associations between CT radiomics analyses and kidney biopsy in patients with kidney disease.BMC nephrology · 2026Article
- The impact of uncertainty estimation on radiomic segmentation reproducibility and scan-rescan repeatability in kidney MRI.Medical physics · 2025Article
- Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease.Scientific reports · 2025Article
- Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.BMC medical imaging · 2024Article
- Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.BMC medical imaging · 2024Article
- Characterization of motion dependent magnetic field inhomogeneity for DWI in the kidneys.Magnetic resonance imaging · 2023Article
- Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review.Computational and structural biotechnology journal · 2023Review
- Diffusion-Weighted Magnetic Resonance Imaging: Clinical Potential and Applications.Journal of clinical medicine · 2022Article
- Artificial Intelligence in Nephrology: Pioneering Precision with Multimodal Intelligence.Indian journal of nephrologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Given the central role of interstitial fibrosis in disease progression in chronic kidney disease (CKD), a role for diffusion-weighted MRI has been pursued. We evaluated the feasibility and preliminary efficacy of using radiomic features to phenotype apparent diffusion coefficient (ADC) maps and hence to the clinical classification(s) of the participants. The study involved 40 individuals (10 healthy and 30 with CKD (eGFR < 60 mL/min/1.73 m2)). Machine learning methods, such as hierarchical clustering and logistic regression, were used. Clustering resulted in the identification of two clusters, one including all individuals with CKD (n = 17), while the second one included all the healthy volunteers (n = 10) and the remaining individuals with CKD (n = 13), resulting in 100% specificity. Logistic regression identified five radiomic features to classify participants as with CKD vs. healthy volunteers, with a sensitivity and specificity of 93% and 70%, respectively, and an AUC of 0.95. Similarly, four radiomic features were able to classify participants as rapid vs. non-rapid CKD progressors among the 30 individuals with CKD, with a sensitivity and specificity of 71% and 43%, respectively, and an AUC of 0.75. These promising preliminary data should support future studies with larger numbers of participants with varied disease severity and etiologies to improve performance.
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.