» Articles » PMID: 38143088

Value of T1 Mapping in the Non-invasive Assessment of Renal Pathologic Injury for Chronic Kidney Disease Patients

Overview
Specialty Radiology
Date 2023 Dec 24
PMID 38143088
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: The objective of this study was to evaluate renal function and pathologic injury in chronic kidney disease (CKD) using T1 mapping.

Methods: We recruited fifteen healthy volunteers (HV) and seventy-five CKD patients to undergo T1 mapping examination, and renal parenchymal T1 values were measured. Spearman correlation analysis was used to evaluate the relevance between the pathologic injury score, estimated glomerular filtration rate (eGFR), and renal parenchymal T1 values. The diagnostic efficiency of T1 value in evaluating renal pathologic impairment was assessed.

Results: In all subjects, renal cortical T1 value was remarkably lower than renal medullary T1 value (P < 0.01). The renal medullary T1 value of HV was considerably lower than that of CKD patients in all stages (P < 0.05). The T1 values were negatively correlated with eGFR (cortex, r = -0.718; medulla, r = -0.645). The T1 values were positively correlated with glomerular injury score (cortex, r = 0.692; medulla, r = 0.632), tubulointerstitial injury score (cortex, r = 0.758; medulla, r = 0.690) (all P < 0.01). The area under the curve (AUC) of renal cortical and medullary T1 values were 0.914 and 0.880 to distinguish moderate-severe from mild renal injury groups. To differentiate mild renal injury group from control group, the AUC values of renal cortical and medullary T1 values were 0.879 and 0.856.

Conclusion: T1 mapping has potential application value in non-invasively assessing renal pathologic injury in CKD.

Citing Articles

A Non-Invasive Technique to Unveil Renal Implications in Anderson-Fabry Disease.

Gravina M, Troise D, Infante B, Tartaglia L, Minopoli B, Allegra C Biomedicines. 2024; 12(9).

PMID: 39335464 PMC: 11428866. DOI: 10.3390/biomedicines12091950.


Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

Chen L, Ren Y, Yuan Y, Xu J, Wen B, Xie S BMC Med Imaging. 2024; 24(1):188.

PMID: 39060984 PMC: 11282691. DOI: 10.1186/s12880-024-01320-6.

References
1.
Gillis K, McComb C, Patel R, Stevens K, Schneider M, Radjenovic A . Non-Contrast Renal Magnetic Resonance Imaging to Assess Perfusion and Corticomedullary Differentiation in Health and Chronic Kidney Disease. Nephron. 2016; 133(3):183-92. DOI: 10.1159/000447601. View

2.
Schmidbauer M, Rong S, Gutberlet M, Chen R, Brasen J, Hartung D . Diffusion-Weighted Imaging and Mapping of T1 and T2 Relaxation Time for Evaluation of Chronic Renal Allograft Rejection in a Translational Mouse Model. J Clin Med. 2021; 10(19). PMC: 8509284. DOI: 10.3390/jcm10194318. View

3.
Mejia-Vilet J, Marquez-Martinez M, Cordova-Sanchez B, Chapa Ibarguengoitia M, Correa-Rotter R, Morales-Buenrostro L . Simple risk score for prediction of haemorrhagic complications after a percutaneous renal biopsy. Nephrology (Carlton). 2017; 23(6):523-529. DOI: 10.1111/nep.13055. View

4.
Liu Z, Xu Y, Zhang J, Zhen J, Wang R, Cai S . Chronic kidney disease: pathological and functional assessment with diffusion tensor imaging at 3T MR. Eur Radiol. 2014; 25(3):652-60. DOI: 10.1007/s00330-014-3461-x. View

5.
Wu H, Jia H, Zhang Y, Liu L, Xu D, Sun H . Monitoring the progression of renal fibrosis by T2-weighted signal intensity and diffusion weighted magnetic resonance imaging in cisplatin induced rat models. Chin Med J (Engl). 2015; 128(5):626-31. PMC: 4834773. DOI: 10.4103/0366-6999.151660. View