ArticleEuropean radiology2023
Value of multiparametric magnetic resonance imaging for evaluating chronic kidney disease and renal fibrosis.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis.Renal failure · 2024Pooled it
- Renal cortical T1 times at 1.5 T: exploratory reference estimates from a real-world cohort with preserved renal function.European journal of radiology open · 2026Article
- Virtual MR elastography with diffusion-weighted imaging to evaluate chronic kidney disease fibrosis and dysfunction.Radiology advances · 2026Article
- Integrating kidney imaging for risk prediction, therapeutic monitoring, and prognostication across the kidney disease spectrum: a review of emerging evidence.Clinical kidney journal · 2026Review
- Capability of multiparametric MRI to detect diabetic nephropathy in high-risk patients with type 2 diabetes: a prospective preliminary study.BMC medical imaging · 2026Article
- Value of multiparametric MRI as a potential marker to differentiate membranous nephropathy from IgA nephropathy.BMC medical imaging · 2026Article
- Machine Learning with Multiparametric MRI and Clinical Biomarkers for Noninvasive Renal Interstitial Fibrosis Staging.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative MRI of pediatric chronic kidney disease and renal fibrosis: potential non-invasive clinical biomarkers.Pediatric radiology · 2026Review
- Assessing kidney fibrosis via multiparametric MRI in native kidney and allografts.Clinical kidney journal · 2026Article
- Nomograms with arterial spin labeling for diagnosing early-stage chronic kidney disease.International urology and nephrology · 2026Article
- Multimodal MRI-based two-stage artificial intelligence framework for renal fibrosis classification in chronic kidney disease.BMC nephrology · 2026Article
- Dual-center validation of a multi-sequence MRI radiomics nomogram for noninvasive assessment of renal fibrosis in chronic kidney disease.BMC medical imaging · 2026Article
- Quantitative assessment of renal function and perfusion changes in membranous nephropathy using multiparametric magnetic resonance imaging.Insights into imaging · 2026Article
- The role of texture analysis of T1 weighted images in diagnosis of chronic kidney disease.Scientific reports · 2026Article
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Assessment of renal insufficiency in patients with renal artery stenosis by multiparametric magnetic resonance imaging.Renal failure · 2025Article
- Noninvasive grading of renal interstitial fibrosis and prediction of annual renal function loss in chronic kidney disease: the optimal solution of seven MR diffusion models.Renal failure · 2025Article
- Progress of functional magnetic resonance imaging in the assessment of transplanted kidney function.Renal failure · 2025Review
- Delta corticomedullary apparent diffusion coefficient on MRI as a biomarker for prognosis in IgA nephropathy.Renal failure · 2025Article
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10 authors at 4 institutions in 2 countries.
Funding
Abstract
objectivesTo identify optimized MRI markers for evaluating chronic kidney disease (CKD) and renal interstitial fibrosis (IF). MATERIALS AND
methodsThis prospective study included 43 patients with CKD and 20 controls. The CKD group was divided into mild and moderate-to-severe subgroups based on pathological results. Scanned sequences included T1 mapping, R2* mapping, intravoxel incoherent motion imaging, and diffusion-weighted imaging. One-way analyses of variance were used to compare MRI parameters among groups. Correlations of MRI parameters with estimated glomerular filtration rate (eGFR) and renal IF were analyzed using age as covariates. The support vector machine (SVM) model was used to evaluate the diagnostic efficacy of multiparametric MRI.
resultsCompared to control values, renal cortical apparent diffusion coefficient (cADC), medullary ADC (mADC), cortical pure diffusion coefficient (cDt), medullary Dt (mDt), cortical shifted apparent diffusion coefficient (csADC), and medullary sADC (msADC) values gradually decreased in the mild and moderate-to-severe groups, while cortical T1 (cT1) and medullary T1 (mT1) values gradually increased. Values of cADC, mADC, cDt, mDt, cT1, mT1, csADC, and msADC were significantly associated with eGFR and IF (p < 0.001). The SVM model indicated that multiparametric MRI combining cT1 and csADC can distinguish patients with CKD from controls with high accuracy (0.84), sensitivity (0.70), and specificity (0.92) (AUC: 0.96). Multiparametric MRI combining cT1 and cADC exhibited high accuracy (0.91), sensitivity (0.95), and specificity (0.81) for evaluating IF severity (AUC: 0.96).
conclusionMultiparametric MRI combining T1 mapping and diffusion imaging may be of clinical utility in non-invasive assessment of CKD and IF. CLINICAL RELEVANCE STATEMENT: This study shows that multiparametric MRI combining T1 mapping and diffusion imaging may be clinically useful in the non-invasive assessment of chronic kidney disease (CKD) and interstitial fibrosis; this could provide information for risk stratification, diagnosis, treatment, and prognosis. KEY POINTS: • Optimized MRI markers for evaluating chronic kidney disease and renal interstitial fibrosis were investigated. • Renal cortex/medullary T1 values increased as interstitial fibrosis increased; cortical shifted apparent diffusion coefficient (csADC) correlated significantly with eGFR and interstitial fibrosis. • Support vector machine (SVM) combining cortical T1 (cT1) and csADC/cADC effectively identifies chronic kidney disease and accurately predicts renal interstitial fibrosis.
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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.