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Eikefjord E, Andersen E, Hodneland E, Hanson EA, Sourbron S, Svarstad E, Lundervold A, Rørvik JT. Dynamic contrast-enhanced MRI measurement of renal function in healthy participants. Acta Radiol 2017; 58:748-757. [PMID: 27694276 DOI: 10.1177/0284185116666417] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background High repeatability, accuracy, and precision for renal function measurements need to be achieved to establish renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a clinically useful diagnostic tool. Purpose To investigate the repeatability, accuracy, and precision of DCE-MRI measured renal perfusion and glomerular filtration rate (GFR) using iohexol-GFR as the reference method. Material and Methods Twenty healthy non-smoking volunteers underwent repeated DCE-MRI and an iohexol-GFR within a period of 10 days. Single-kidney (SK) MRI measurements of perfusion (blood flow, Fb) and filtration (GFR) were derived from parenchymal intensity time curves fitted to a two-compartment filtration model. The repeatability of the SK-MRI measurements was assessed using coefficient of variation (CV). Using iohexol-GFR as reference method, the accuracy of total MR-GFR was determined by mean difference (MD) and precision by limits of agreement (LoA). Results SK-Fb (MR1, 345 ± 84; MR2, 371 ± 103 mL/100 mL/min) and SK-GFR (MR1, 52 ± 14; MR2, 54 ± 10 mL/min/1.73 m2) measurements achieved a repeatability (CV) in the range of 15-22%. With reference to iohexol-GFR, MR-GFR was determined with a low mean difference but high LoA (MR1, MD 1.5 mL/min/1.73 m2, LoA [-42, 45]; MR2, MD 6.1 mL/min/1.73 m2, LoA [-26, 38]). Eighty percent and 90% of MR-GFR measurements were determined within ± 30% of the iohexol-GFR for MR1 and MR2, respectively. Conclusion Good repeatability of SK-MRI measurements and good agreement between MR-GFR and iohexol-GFR provide a high clinical potential of DCE-MRI for renal function assessment. A moderate precision in MR-derived estimates indicates that the method cannot yet be used in clinical routine.
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Affiliation(s)
- Eli Eikefjord
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Erling Andersen
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Erlend Hodneland
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Christian Michelsen Research (CMR) AS, Bergen, Norway
| | - Erik A Hanson
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Steven Sourbron
- Faculty of Medicine and Health, University of Leeds, Leeds, UK
| | - Einar Svarstad
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - Arvid Lundervold
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Jarle T Rørvik
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Quantification of Single-Kidney Function and Volume in Living Kidney Donors Using Dynamic Contrast-Enhanced MRI. AJR Am J Roentgenol 2016; 207:1022-1030. [DOI: 10.2214/ajr.16.16168] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT). Clin Exp Nephrol 2015; 19:974-81. [PMID: 25618493 DOI: 10.1007/s10157-015-1082-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 01/12/2015] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND PURPOSE The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model. PATIENTS AND METHODS Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models. RESULTS The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (p < 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (r = 0.58, p < 0.01) and %RPV preservation (r = 0.54, p < 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 - 0.55(age) - 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (r = 0.83; p < 0.001). The external validation cohort (n = 21) showed our model outperformed previously reported models. CONCLUSIONS Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.
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Abstract
Renal cell carcinoma (RCC) is most commonly diagnosed as an incidental finding on cross-sectional imaging and represents a significant clinical challenge. Although most patients have a surgically curable lesion at the time of diagnosis, the variability in the biologic behavior of the different histologic subtypes and tumor grade of RCC, together with the increasing array of management options, creates uncertainty for the optimal clinical approach to individual patients. State-of-the-art magnetic resonance imaging (MRI) provides a comprehensive assessment of renal lesions that includes multiple forms of tissue contrast as well as functional parameters, which in turn provides information that helps to address this dilemma. In this article, we review this evolving and increasingly comprehensive role of MRI in the detection, characterization, perioperative evaluation, and assessment of the treatment response of renal neoplasms. We emphasize the ability of the imaging "phenotype" of renal masses on MRI to help predict the histologic subtype, grade, and clinical behavior of RCC.
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Affiliation(s)
- Naomi Campbell
- Department of Radiology, Center for Biomedical Imaging, NYU Langone Medical Center, New York, NY
| | - Andrew B. Rosenkrantz
- Department of Radiology, Center for Biomedical Imaging, NYU Langone Medical Center, New York, NY
| | - Ivan Pedrosa
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX
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