Prediction of early renal allograft dysfunction using multiparametric magnetic resonance imaging

Introduction

Chronic kidney disease (CKD) is a significant public health issue, affecting approximately 10% of the global population (1). It is responsible for millions of deaths each year, and hundreds of thousands of CKD patients require dialysis to sustain life (2). Kidney transplantation is the main treatment of choice for end-stage CKD patients. However, acute rejection threatens the transplanted kidney in about 8.4% of patients at 3 months post-transplantation (3). Renal allograft injuries are caused by various insults that lead to renal fibrosis, which is pathologically characterized by the extensive deposition of the extracellular matrix, and an imbalance between tubular injury-related repair mechanisms and chronic injury stimuli (4). Evaluating the burden of fibrosis can help identify high-risk patients who may develop significant renal allograft dysfunction, guide appropriate anti-fibrosis interventions, and monitor treatment outcomes (5).

Currently, renal allograft biopsy is the gold standard for detecting renal fibrosis in kidney recipients. However, as an invasive procedure, it carries risks such as bleeding, infections, perirenal hematomas, and even death, making it impractical for frequent clinical use (6). Additionally, biopsies are subject to sampling errors and may not accurately represent the overall state of the kidney. Therefore, non-invasive imaging biomarkers have been proposed to evaluate fibrosis and inflammation in renal allografts.

Multiparametric magnetic resonance imaging (mpMRI), which includes techniques such as diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, longitudinal relaxation time (T1) mapping, and arterial spin labeling (ASL) perfusion (7), provides a comprehensive evaluation of the renal structure, microstructure, and hemodynamics. Preliminary findings suggest that ASL can quantify renal blood flow (RBF), which is associated with the plasma clearance of aminopurine acid (8). Non-invasive tissue characterization through voxel-wise mapping of the T1 can indicate alterations in tissue fibrosis (9). Renal allograft injury is usually accompanied by changes in renal blood perfusion and microstructure, which can be detected by ASL and T1 mapping (10,11). The clinical application of ASL to transplanted kidneys raises several technical considerations, such as ensuring that the tagging of the RBF is orthogonal to the transplanted renal artery, and minimizing motion artifacts to reduce misregistration for the reliable quantification of cerebral blood flow (12).

Recently, T1-rho (T1ρ) imaging has emerged as a viable approach for tissue characterization. T1ρ, also known as the spin-lattice relaxation time in the rotating frame, provides a magnetic resonance (MR) contrast mechanism that can probe slow molecular motional processes in the kilohertz range, including proteins such as collagen and amyloid. Unlike conventional T1 and T2 mapping, T1ρ imaging can detect interstitial fibrosis directly (rather than indirectly) through its effects on water. It has been used in ischemic heart disease to differentiate between normal and infarcted myocardium, and has shown the ability to detect ventricular fibrosis (13). T1ρ imaging may be useful in identifying the early stages of renal fibrosis, and may serve as a more sensitive and specific biomarker than traditional techniques (14). It has been used in animal models, and is considered a promising imaging biomarker for renal allograft fibrosis (15).

This study aimed to apply mpMRI to kidney transplant recipients and evaluate which MR imaging (MRI) techniques, including DWI, T1ρ mapping, and ASL, could serve as non-invasive biomarkers for predicting renal allograft dysfunction at an early stage. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2182/rc).

Methods

Study participants

This single-center prospective study was approved by the Ethics Committee of the China-Japan Friendship Hospital (No. 2022-KY-236), and informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. To be eligible for inclusion in the study, the patients had to meet the following inclusion criteria: (I) have early graft function after kidney transplantation; (II) have undergone mpMRI 1 month after transplantation; and (III) have had their creatinine levels continuously monitored for 3 months at our outpatient department. From September 2022 to July 2024, 37 adult kidney transplant recipients were identified for inclusion in the study. Of the 37 patients initially identified, six were excluded (one refused to provide informed consent, one had claustrophobia, three could not complete the examination due to physical discomfort, and one had poor respiratory coordination resulting in image artifacts) (Figure 1).

Figure 1 Study flowchart. mpMRI, multiparametric magnetic resonance imaging.

The serum creatinine levels of each patient were recorded at the baseline (1 month after transplantation) and at follow-up (3 months after transplantation) (16). The patients were divided into a stable group (SG) and an impaired group (IG) based on whether the serum creatinine level at 3 months post-transplantation increased by more than 25% compared to that at the first month post-transplantation (17).

MRI protocols

MRI was performed approximately 1 month after transplantation using a 1.5-T MR scanner (Ingenia Ambition, Philips Healthcare, Best, Netherlands), equipped with a 32-channel body coil. For the morphologic evaluation, axial T2- and T1-weighted images were acquired, with the scan range extending from the L3 vertebra to the pubic symphysis. For functional MRI, three-dimensional (3D) pulsed ASL [label duration: 1,800 ms; post-label delay (PLD): 1,600 ms], axial DWI (b values: 0 and 800 s/mm²) and T1ρ mapping [time of spin locking (TSL): 0, 13.3, 26.6, and 40 ms] were performed. For the pseudo-continuous ASL (pCASL) sequence, 3D gradient and spin-echo (SE) acquisition was performed with a two-dimensional SE echo-planar imaging sequence with background suppression. The labeling plane was 8 cm above the transplanted kidney, oriented axially and perpendicular to the abdominal aorta. Labeling was applied for 1.8 seconds followed by a PLD of 1,600 ms before image acquisition. The mpMRI images of the transplanted kidneys were all acquired in the axial planes. The image parameters for pCASL, T1ρ mapping, and DWI are listed in Table 1.

Imaging analysis

ADC maps, RBF maps, and T1ρ maps were generated automatically with Intellispace Portal (version 10, Philips Healthcare) for parameter measurement. Two radiologists, with 8 and 10 years of abdominal imaging experience, independently and randomly analyzed all the images without referring to the clinical or pathological data. To measure ADC, RBF, and T1ρ, the RBF maps, ADC maps, and T1ρ maps were first registered to T2-weighted images. Three irregular regions of interest (ROIs), ranging from 110 to 150 mm², were then drawn on the cortex area of the T2-weighted images outlining the configuration of the cortex, and copied onto the functional maps for measurement (Figure 2). For each transplanted kidney, three ROIs were drawn at the upper pole, interpolar portion, and lower pole, and measurements were undertaken at three middle continuous slices. Areas with blood vessels, hematomas, and cysts were excluded from the ROIs. The values from the total nine ROIs were averaged for each transplanted kidney for the statistical analysis.

Figure 2 MpMRI images. The upper row shows the images of a 58-year-old female patient from the SG, and the lower row shows the images of a 35-year-old male patient from the IG, 1 month post-transplantation. The green lines outline three irregular ROIs drawn on the renal cortex area. ROIs are first drawn on the T2W images and then copied onto the ADC, T1ρ, and RBF maps for parameter measurement. The ROIs are not shown on the T1ρ and RBF maps. ADC, apparent diffusion coefficient; IG, impaired group; mpMRI, multiparametric magnetic resonance imaging; RBF, renal blood flow; ROIs, regions of interest; SG, stable group; T1ρ, T1-rho; T2W, T2-weighted.

Statistical analysis

Each patient was independently assessed by two radiologists. The mean value from both readers was used as the final value for each patient. SPSS (SPSS 29.0, SPSS Inc., Chicago, IL, USA), MedCalc (version 15.0, Mariakerke, Belgium), and GraphPad Prism (version 10.4.1, GraphPad Software) were used for statistical analysis and plotting. Data distribution was assessed for normality using the normality test and distribution curve before further statistical analysis. The data are presented as the mean ± standard deviation. The Chi-squared test was used to examine the gender ratio. The t-test was used to compare the two groups. The Chi-squared test was used to assess differences among the three groups. A logistic regression analysis was used to investigate the association between the MRI parameters and serum creatinine levels at follow-up. Odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated. A receiver operating characteristic (ROC) curve analysis was used to evaluate the sensitivity, specificity, and cut-off values of the MRI parameters for the prognostic indices. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC), with values less than 0.5, values between 0.5 and 0.75, values between 0.75 and 0.9, and values greater than 0.90 indicating poor, moderate, good, and excellent reliability, respectively (18). A P value <0.05 was considered statistically significant.

Results

Patient characteristics

MpMRI was performed on 31 patients (25 males and 6 females, mean age: 44.4±10.9 years), including 20 with stable graft function (the SG) and 11 with impaired graft function (the IG). The mean serum creatinine levels 1 and 3 months after transplantation were 196.1±77.8 and 183.1±116.4 µmol/L, respectively. The mean serum estimated glomerular filtration rate (eGFR) 1 and 3 months after transplantation were 44.4±11.1 and 49.2±11.2 µmol/L, respectively. At 1 month post-transplantation, there were no significant differences between the two groups in terms of age, gender, serum creatinine level, or eGFR (P>0.05). At 3 months post-transplantation, the serum creatinine levels were significantly higher and the eGFR was lower in the IG than the SG (P<0.001). The demographic and clinical information of the patients are presented in Table 2.

The stability of the T1ρ measurements

The stability of T1ρ measurements was assessed across different renal regions, including the upper, interpolar, and lower poles. Chi-squared tests were used to compare the average T1ρ values of the upper, interpolar, and lower poles, and the results revealed no statistically significant differences (108±13.2 vs. 113±21.1 vs. 105±15.4 ms, F=1.835, P=0.17) among them.

Comparative analysis of the clinical characteristics and MRI parameters

The mean ADC value of all 31 patients was 2.0±0.20⁻³ mm²/s, the mean RBF was 242.3±64.5 mL/100 g/min, and the mean T1ρ mapping was 109.8±18.9 ms. All parameters had good inter-observer reproducibility (ICCs of 0.86, 0.93, and 0.91 for ADC, RBF, and T1ρ, respectively). As Figure 3 shows, at 1 month post-transplantation, the ADC values did not differ significantly between the two groups (P>0.05). However, significant differences in RBF (261.1±56.4 vs. 208.2±66.6 mL/100 g/min, P=0.04) and T1ρ (116.1±20.0 vs. 98.3±9.7 ms, P=0.01) were observed between the SG and IG. The MRI parameters of the two groups are summarized in Table 3.

Figure 3 Comparison of RBF (A), ADC (B), and T1ρ (C) between the IG and the SG. ADC, apparent diffusion coefficient; IG, impaired group; RBF, renal blood flow; SG, stable group; T1ρ, T1-rho.

The prognostic evaluation value of the MRI parameters

In the binary logistic regression analysis, among all the measured imaging parameters (i.e., RBF, ADC, and T1ρ), RBF and T1ρ were significantly correlated with renal function at 3 months post-transplantation (P<0.05) (Table 4). The ROC curve analysis revealed that RBF values greater than 192.4 mL/100 g and T1ρ values greater than 103.2 ms significantly decreased the risk of renal dysfunction, yielding the highest Youden index values. For RBF with a cut-off value of 192.4 mL/100 g, the area under the curve (AUC), sensitivity, and specificity were 0.739, 100%, and 55%, respectively. For T1ρ with a cut-off value of 103.2 ms, the AUC, sensitivity, and specificity were 0.845, 85%, and 73%, respectively (Table 4). The AUC for the combination of RBF and T1ρ was 0.927 (Figure 4).

Figure 4 ROC curves for mpMRI parameters (i.e., RBF, T1ρ, and RBF + T1ρ). The AUCs for distinguishing between the SG from the IG were 0.739 for RBF, 0.845 for T1ρ, and 0.927 for the combination of RBF and T1ρ. AUCs, areas under the curve; CI, confidence interval; IG, impaired group; mpMRI, multiparametric magnetic resonance imaging; RBF, renal blood flow; ROC, receiver operating characteristic; SG, stable group; T1ρ, T1-rho.

Discussion

In this study, we investigated the value of DWI, ASL, and T1ρ mapping in predicting short-term allograft dysfunction. We found that RBF and T1ρ at 1 month post-transplantation were associated with impaired allograft function 2 months later. A single time value related to renal allograft fibrosis is clinically significant; however, monitoring allograft fibrosis over time could be even more relevant for clinical decisions and predicting disease progression. These results highlight the potential role of ASL and T1ρ mapping in assessing allograft dysfunction.

High renal perfusion is crucial for maintaining stable glomerular filtration (19), particularly in renal allografts. Acute rejection, which typically occurs early after kidney transplantation, is often linked to allograft ischemia or hypoperfusion. Changes in renal perfusion are among the earliest markers of renal damage (20). Therefore, assessing renal perfusion can help identify patients with early allograft injuries. ASL-MRI is a reliable tool for detecting RBF with low variability. Previous studies have shown that quantifying changes in perfusion and diffusion may predict long-term renal allograft outcomes (21-23). Compared to reduced field of view intravoxel incoherent motion (IVIM) DWI, ASL may be more effective at non-invasively distinguishing different degrees of renal allograft fibrosis (4). Consistent with previous research, our results also showed that the baseline RBF is associated with short-term allograft dysfunction and has good diagnostic performance. A recent study also found that RBF has high diagnostic and prognostic value for detecting graft dysfunction early after transplantation (16). In our study, the reduced RBF in the allograft kidneys at the baseline might have indicated acute rejection, as we excluded recipients with delayed graft function. However, some studies suggest that while ASL perfusion can indicate renal function, it may not be able to be used to differentiate between fibrotic burden and other damage factors, such as inflammation (4,24,25). These confounding effects may reduce the diagnostic efficiency of RBF for renal allograft fibrosis.

Our results also indicated that T1ρ mapping was effective in predicting short-term renal allograft dysfunction. The use of T1ρ mapping in evaluating myocardial fibrosis is increasingly being studied. Research has revealed a significant correlation between fibrosis fractions measured histologically in ex vivo human hearts and T1ρ values (26). However, studies on T1ρ mapping in renal transplantation are relatively limited. A previous cross-sectional study assessed the utility of T1ρ mapping in evaluating fibrosis in renal allografts, and found that it had an AUC of 0.77 for differentiating between functional and fibrotic allografts. The study showed that cortical T1ρ was significantly correlated with Masson’s trichrome-stained fractions and the eGFR (27). The present study did not include pathological evidence; however, it showed the utility of T1ρ mapping in predicting allograft dysfunction through follow-up. Further, the predictive power of T1ρ mapping appeared to be superior to that of ASL. Notably, ASL reflects allograft perfusion, while T1ρ mapping captures macromolecule-water interactions and collagen content. Thus, complicated structural abnormalities may begin early during acute rejection, affecting the change in T1ρ. However, as biopsies were not performed at this early stage, the extent of renal fibrosis formation could not be determined.

Contrary to the findings of previous studies (28), in the present study, ADC was not found to be significantly associated with renal allograft function. Friedli et al. found that in animal models, both T1 and ADC were correlated with fibrosis and inflammation based on histological findings (29). However, for kidney allograft recipients, the absolute cortical ADC or T1 values were poorly correlated with interstitial fibrosis assessed by biopsy. Conversely, the corticomedullary ADC difference was independently associated with kidney fibrosis (30), and changes in this difference paralleled increases in fibrosis identified by biopsies (31). Image data for the renal medulla were available in our study; however, we chose to focus on the cortical measurements. The glomeruli and proximal tubules are primarily located in the renal cortex, which receives 85–90% of the kidney’s total blood flow. This high perfusion renders the cortex particularly susceptible to ischemic injury and toxic insults. Dysfunction of the glomeruli leads to a decline in the filtration rate, as seen in acute kidney injury, while damage to the proximal tubules impairs reabsorption, resulting in abnormal urine composition (32). Thus, renal dysfunction typically begins in these two critical structures. Given this, we chose to focus exclusively on measuring the ADC of the cortex of the transplanted kidney.

The combination of RBF and T1ρ was better able to predict allograft dysfunction than either parameter alone. Our findings align with a recent study that showed that mpMRI significantly enhances diagnostic and prognostic values for detecting graft dysfunction compared to the eGFR (31). Despite the high diagnostic and prognostic potential of mpMRI, technical factors and clinical convenience must be considered. A fixed PLD of 1,600 ms was selected in our study based on a previous study (33) that identified 1,600 ms as optimal for assessing renal perfusion in transplanted kidneys. Coronal oblique slices (along the major axis of the kidneys) are recommended for renal ASL (34) but are not suitable for transplanted kidneys. Axial planes may not strictly align with the major axis of the transplanted kidneys. Therefore, there may be technical errors regarding ASL data acquisition. Moreover, ASL sequences, high-resolution DWI, and IVIM imaging require several minutes to collect raw data, and motion artifacts can lead to quantification inaccuracies during postprocessing. Conversely, T1ρ mapping, which generates tissue contrasts through endogenous T1ρ relaxation during a breath hold, is relatively rapid and convenient for clinical use. The stability of T1ρ measurements was also shown across different renal regions, including the upper, interpolar, and lower poles.

This study had several limitations. First, the relatively small sample size might have introduced sampling errors into the statistical analysis. Larger studies need to be conducted to confirm the role of T1ρ as a biomarker for renal fibrosis. Second, our study relied on serum creatinine levels as an indicator of graft function, but these levels might not fully represent the underlying tissue state. While T1ρ mapping and ASL provide non-invasive assessments of fibrosis and perfusion, their correlation with histopathological changes requires further validation. Future studies incorporating biopsy data need to be conducted to establish a direct relationship between T1ρ mapping and renal tissue characteristics. Finally, renal medullary values were not measured in this study. It has been reported that medullary measurements have relatively lower reproducibility than cortical measurements (21). Nonetheless, we believe that cortical parameters are adequate for assessing renal allograft function.

Conclusions

Our study used mpMRI to assess renal allograft dysfunction. RBF and T1ρ measured 1 month after transplantation demonstrated good performance in predicting early allograft dysfunction, potentially due to acute rejection. T1ρ mapping in particular shows promise for evaluating early renal structural changes, as it outperforms ASL and is more convenient for clinical application.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2182/rc

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2182/coif). X.Y. is an employee of Philips Healthcare. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was approved by the Ethics Committee of the China-Japan Friendship Hospital (No. 2022-KY-236), and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Informed consent was obtained from all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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