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Friedli I, Baid-Agrawal S, Unwin R, Morell A, Johansson L, Hockings PD. Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease. J Clin Med 2023; 12:4625. [PMID: 37510740 PMCID: PMC10380287 DOI: 10.3390/jcm12144625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.
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Affiliation(s)
- Iris Friedli
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden
| | - Seema Baid-Agrawal
- Transplant Center, Sahlgrenska University Hospital, University of Gothenburg, 41345 Gothenburg, Sweden
| | - Robert Unwin
- AstraZeneca R&D BioPharmaceuticals, Translational Science and Experimental Medicine, Early Cardiovascular, Renal & Metabolic Diseases (CVRM), Granta Park, Cambridge CB21 6GH, UK
| | - Arvid Morell
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden
| | | | - Paul D Hockings
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden
- MedTech West, Chalmers University of Technology, 41345 Gothenburg, Sweden
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Dillman JR, Benoit SW, Gandhi DB, Trout AT, Tkach JA, VandenHeuvel K, Devarajan P. Multiparametric quantitative renal MRI in children and young adults: comparison between healthy individuals and patients with chronic kidney disease. Abdom Radiol (NY) 2022; 47:1840-1852. [PMID: 35237897 DOI: 10.1007/s00261-022-03456-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE Multiparametric quantitative renal MRI may provide noninvasive radiologic biomarkers of chronic kidney disease (CKD) based on investigations in animal models and adults. We aimed to (1) obtain normative multiparametric quantitative MRI data from the kidneys of healthy children and young adults, (2) compare MRI measurements between healthy control participants and patients with CKD, and (3) determine if MRI measurements correlate with clinical and laboratory data as well as histology. METHODS This was a prospective, case-control study of 20 healthy controls and 12 CKD patients who underwent percutaneous renal biopsy ranging from 12 to 23 years of age between October 2018 and March 2020. Kidney function was documented and pathology assessed for fibrosis/inflammation. Utilizing a field strength of 1.5T, we examined renal T1, T2, and T2* relaxation mapping, MR elastography (MRE), and diffusion-weighted imaging (DWI). A single analyst made all manual measurements for quantitative MRI pulse sequences. Independent measurements from cortex, medulla, and whole kidney were obtained by drawing regions of interest on single slices from the upper, mid, and lower kidney. A weighted average was calculated for each kidney; if two kidneys, the right and left were averaged. Continuous variables were compared with Mann-Whitney U test; bivariate relationships were assessed using Spearman rank-order correlation. RESULTS Median estimated glomerular filtration rate (eGFR) was 112.3 ml/min/1.73 m2 in controls (n = 20, 10 females) and 55.0 ml/min/m2 in CKD patients (n = 12, 2 females) (p < 0.0001). Whole kidney (1333 vs. 1291 ms; p = 0.018) and cortical (1212 vs 1137 ms; p < 0.0001) T1 values were higher in CKD patients. Cortical T1 values correlated with eGFR (rho = - 0.62; p = 0.0003) and cystatin C (rho = 0.58; p = 0.0007). Whole kidney (1.87 vs. 2.02 10-3 mm2/s; p = 0.007), cortical (1.89 vs. 2.04 10-3 mm2/s; p = 0.008), and medullary (1.87 vs. 1.98 10-3 mm2/s; p = 0.0095) DWI apparent diffusion coefficients (ADC) were lower in CKD patients. Whole kidney ADC correlated with eGFR (rho = 0.45; p = 0.012) and cystatin C (rho = - 0.46; p = 0.009). Cortical histologic inflammation correlated with DWI ADC (rho = - 0.71; p = 0.011). CONCLUSION Renal T1 relaxation and DWI ADC measurements differ between pediatric healthy controls and CKD patients, correlate with laboratory markers of CKD, and may have histologic correlates.
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Affiliation(s)
- Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Stefanie W Benoit
- Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Deep B Gandhi
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jean A Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Katherine VandenHeuvel
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Pathology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pathology and Laboratory Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Prasad Devarajan
- Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Ishikawa M, Inoue T, Kozawa E, Okada H, Kobayashi N. Framework for estimating renal function using magnetic resonance imaging. J Med Imaging (Bellingham) 2022; 9:024501. [PMID: 35360418 PMCID: PMC8923691 DOI: 10.1117/1.jmi.9.2.024501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 02/23/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an examination/treatment policy is determined. A framework for the estimation of renal function from water images obtained using the Dixon method is proposed to provide information that helps clinicians reach a diagnosis by accurately estimating renal function on the basis of renal MRI. Approach: The proposed framework consists of four steps. First, the kidney area is extracted by MRI using the Dixon method with a U-net by deep learning. Second, the extracted renal region is registered with the target mask. Third, the kidney features are calculated based on the target mask classification information created by a specialist. Fourth, the estimated glomerular filtration rate (eGFR) representing the renal function is estimated using a regression support vector machine from the calculated features. Results: For the accuracy evaluation, we conducted an experiment to estimate the eGFR when MRI was performed and the eGFR slope, which is the annual rate of decline in eGFR. When the accuracy was evaluated for 165 subjects, the eGFR was estimated to have a root mean square error (RMSE) of 11.99 and a correlation coefficient of 0.83. Moreover, the eGFR slope was estimated to have an RMSE of 4.8 and a correlation coefficient of 0.5. Conclusions: Therefore, the proposed method shows the possibility of estimating the prognosis of renal function based on water images obtained by the Dixon method.
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