ArticleJournal of magnetic resonance imaging : JMRI2025
Combining Multifrequency Magnetic Resonance Elastography With Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease.
Article in Journal of magnetic resonance imaging : JMRI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Virtual MR elastography with diffusion-weighted imaging to evaluate chronic kidney disease fibrosis and dysfunction.Radiology advances · 2026Article
- Kidney Elastography in Adult Nephrology: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Detection and classification of medical images using deep learning for chronic kidney disease.International urology and nephrology · 2026Article
- Electroacupuncture enhances mesenchymal stem cell therapy via improved perfusion and inflammation modulation in peripheral nerve injury: an IVIM-MRI study in rats.Frontiers in neurology · 2025Article
- Artificial intelligence in chronic kidney disease: Bibliometric and visual analysis of trends and future directions.Digital healthArticle
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Abstract
backgroundMultifrequency MR elastography (mMRE) enables noninvasive quantification of renal stiffness in patients with chronic kidney disease (CKD). Manual segmentation of the kidneys on mMRE is time-consuming and prone to increased interobserver variability. PURPOSE: To evaluate the performance of mMRE combined with automatic segmentation in assessing CKD severity. STUDY TYPE: Prospective.
participantsA total of 179 participants consisting of 95 healthy volunteers and 84 participants with CKD. FIELD STRENGTH/SEQUENCE: 3 T, single shot spin echo planar imaging sequence. ASSESSMENT: Participants were randomly assigned into training (n = 58), validation (n = 15), and test (n = 106) sets. Test set included 47 healthy volunteers and 58 CKD participants with different stages (21 stage 1-2, 22 stage 3, and 16 stage 4-5) based on estimated glomerular filtration rate (eGFR). Shear wave speed (SWS) values from mMRE was measured using automatic segmentation constructed through the nnU-Net deep-learning network. Standard manual segmentation was created by a radiologist. In the test set, the automatically segmented renal SWS were compared between healthy volunteers and CKD subgroups, with age as a covariate. The association between SWS and eGFR was investigated in participants with CKD. STATISTICAL TESTS: Dice similarity coefficient (DSC), analysis of covariance, Pearson and Spearman correlation analyses. P < 0.05 was considered statistically significant.
resultsMean DSCs between standard manual and automatic segmentation were 0.943, 0.901, and 0.970 for the renal cortex, medulla, and parenchyma, respectively. The automatically quantified cortical, medullary, and parenchymal SWS were significantly correlated with eGFR (r = 0.620, 0.605, and 0.640, respectively). Participants with CKD stage 1-2 exhibited significantly lower cortical SWS values compared to healthy volunteers (2.44 ± 0.16 m/second vs. 2.56 ± 0.17 m/second), after adjusting age.
conclusionmMRE combined with automatic segmentation revealed abnormal renal stiffness in patients with CKD, even with mild renal impairment. PLAIN LANGUAGE SUMMARY: The renal stiffness of patients with chronic kidney disease varies according to the function and structure of the kidney. This study integrates multifrequency magnetic resonance elastography with automated segmentation technique to assess renal stiffness in patients with chronic kidney disease. The findings indicate that this method is capable of distinguishing between patients with chronic kidney disease, including those with mild renal impairment, while simultaneously reducing the subjectivity and time required for radiologists to analyze images. This research enhances the efficiency of image processing for radiologists and assists nephrologists in detecting early-stage damage in patients with chronic kidney disease. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.
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