The application of shear wave elastography in monitoring renal allografts

Introduction

Chronic kidney disease consists of five stages (1), and when the disease progresses to stage 5 (i.e., end-stage renal disease), patients require replacement therapies such as dialysis or renal transplantation (2). Renal transplantation is currently the optimal treatment modality (3), as it confers a superior quality of life to patients. In this procedure, the monitoring of renal allograft function in the postoperative period is of particular importance. Renal transplant recipients are followed up with laboratory tests and color Doppler ultrasound examination. Ultrasound is routinely used for noninvasive monitoring of renal allografts, assessing cortical and medullary echogenicity and measuring intrarenal hemodynamic parameters. However, these sonographic findings are often nonspecific and may not reliably reflect underlying pathological changes, particularly in early or subclinical disease. In cases where ultrasound results are abnormal or clinical suspicion persists, percutaneous biopsy remains the gold standard approach for diagnosing allograft dysfunction. Yet, as an invasive procedure associated with risks such as bleeding or arteriovenous fistula, biopsy is not suitable for routine or repeated surveillance. This highlights the need for noninvasive parameters that can complement conventional ultrasound in the monitoring of renal allograft function.

Elastography, a noninvasive tool, can be categorized into two main types based on its underlying working principles: strain elastography and shear wave elastography (SWE) (4,5). SWE can be used to examine certain organs, such as the thyroid, breast, and liver. A number of studies have used elastography to assess the function of allograft kidneys (6-8). In our study, we evaluated the ability of SWE-based graft stiffness to noninvasively assess renal allograft function and developed a nomogram incorporating shear wave velocity (SWV) and clinical factors. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0014/rc).

Methods

This study was approved by the Biomedical Ethics Review Committee of West China Hospital of Sichuan University, and was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All patients provided written informed consent prior to their participation in the study.

The research population consisted of patients who accepted a renal allograft and who were followed up at the Ultrasound Department of West China Hospital of Sichuan University from December 2021 to August 2022. The exclusion criteria were as follows: (I) vascular lesions in the renal allograft on ultrasonography; (II) hydronephrosis of the renal allograft; (III) intraparenchymal space-occupying lesions of the renal allograft >1 cm; (IV) renal hilum of the allograft oriented ventrally; (V) measurement failure, defined as the color in the region of interest (ROI) failing to uniformly fill more than 90% of the area in repeated measurements; and (VI) a measurement depth ≤4 cm and a body mass index (BMI) ≤28 kg/m². The flowchart of participant inclusion is provided in Figure 1. Patients emptied their bladders before undergoing the ultrasound examination.

Figure 1 The flowchart of patient inclusion.

In this study, we used an Aixplorer ultrasound system (SuperSonic Imagine, Aix-en-Provence, France) to measure the stiffness of the renal allografts. For stiffness measurements, we placed the XC6-1 probe (SuperSonic Imagine) on the lower pole of the renal allograft in the longitudinal section, positioned the ROI in the cortex, set the diameter of the quantitative box to 8 mm, and asked patients to breathe naturally. Both sonographers, one of whom performed all ultrasound examinations, were at the intermediate professional level, had more than 3 years of experience in renal transplant ultrasound, and had received standardized training. Inter-observer variability was assessed with both sonographers measuring the same patients, and intra-observer reliability was tested by one sonographer remeasuring the same patients after a 30-minute interval. All reliability analyses were conducted on randomly selected patients. Three measurements were obtained each time, and their median value was used for analysis. Moreover, the resistive index (RI) of the renal artery, segmental artery, arcuate artery, and interlobar artery of the renal allograft was measured. Data were obtained from three repeated measurements, and the median value was used for analysis.

Additionally, laboratory findings, including serum creatinine (Scr), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), uric acid (UA), serum cystatin C (Cys C), cholesterol, and albumin, were collected from patients. Missing data were handled via complete-case analysis, and potential sources of bias were carefully considered during study design and data analysis.

The independent-samples t test or Mann-Whitney U test was used to compare normally or nonnormally distributed continuous variables between two groups, respectively. Normally distributed data are presented as the mean ± standard deviation, and nonnormally distributed data are presented as the median and interquartile range. The association between renal allograft stiffness and laboratory indices was assessed via Spearman rank correlation. The patients were divided into a normal renal allograft function group (eGFR ≥60 mL/min/1.73 m²) and an abnormal group (eGFR <60 mL/min/1.73 m²) according to eGFR, and binary logistic regression was used to identify independent influencing factors for renal allograft dysfunction. Univariate analysis was performed with the Mann-Whitney U test, which was followed by multivariate analysis via binary logistic regression to screen the potential predictors for inclusion in a nomogram. Calibration curves, decision curves, and receiver operating characteristic (ROC) curves were drawn. The area under the curve (AUC) and the optimal cutoff value were calculated. SPSS 25.0 (IBM Corp., Armonk, NY, USA) was applied for correlation, univariate and multivariate analyses, while R 4.5.1 (The R Foundation for Statistical Computing, Vienna, Austria) was used for nomogram construction. A P value <0.05 was defined as statistically significant.

Results

Patient demographics

A total of 462 patients were included in this study. The basic information of the patients is summarized in Table 1. Figure 2 presents the ultrasound elastography images of cases with normal and abnormal renal allograft function.

Figure 2 The ultrasound elastography images of cases with abnormal (A,B) and normal (C,D) renal allograft function. (A) Ultrasound elastography image of a renal transplant recipient with abnormal allograft function (eGFR: 42.42 mL/min/1.73 m²; Scr: 157 µmol/L; serum Cys C: 2.03 mg/L; SWV: 3.7 m/s). (B) Ultrasound elastography image of a renal transplant recipient with abnormal allograft function (eGFR: 52.73 mL/min/1.73 m²; Scr: 110 µmol/L; Cys C: 1.67 mg/L; SWV: 3.0 m/s). (C) Ultrasound elastography image of a renal transplant recipient with normal allograft function (eGFR: 86.91 mL/min/1.73 m²; Scr: 98 µmol/L; Cys C: 1.06 mg/L; SWV: 2.8 m/s). (D) Ultrasound elastography image of a renal transplant recipient with normal allograft function (eGFR: 70.28 mL/min/1.73 m²; Scr: 93 µmol/L; Cys C: 1.03 mg/L; SWV: 2.9 m/s). Cys C, cystatin C; eGFR, estimated glomerular filtration rate; SWV, shear wave velocity.

Consistency test

A total of 43 patients were included in the test of inter-observer variability, and 50 patients were included in the test of intra-observer variability. The interclass coefficient of correlations of SWV, as shown in Table 2, indicated good consistency for the intra-observer and inter-observer measurements.

Correlation analysis

SWV significantly correlated with renal transplantation time (RTT) (ρ=–0.128), Scr (ρ=0.209), eGFR (ρ=–0.234), BUN (ρ=0.128), UA (ρ=0.146), and Cys C (ρ=0.213) (P<0.05). EGFR and RTT were negatively correlated with SWV, while Scr, BUN, UA, and Cys C were positively correlated with SWV. The correlation between SWV and laboratory index is shown in Table 3.

Nomogram

The comparison of indicators between the normal function group (eGFR ≥60 mL/min/1.73 m²) and the abnormal function group (eGFR <60 mL/min/1.73 m²), along with the results of the multivariate analysis, are presented in Table 4. The variables identified as statistically significant by univariate analysis were as follows: SWV, age, RTT, BUN, Scr, Cys C, UA, serum albumin, and the RI of segmental artery, interlobar artery, and arcuate artery of the renal allograft. The SWV of renal allografts differed significantly between the normal and abnormal groups, with a cutoff value of 3.15 m/s, a sensitivity of 42.1%, and a specificity of 82.9% (AUC =0.645; P<0.05). In the multivariate analysis, SWV, age, Scr, and Cys C were significantly different (P<0.05). The data were randomly split into a training set and a validation set at a 7:3 ratio. Based on whether eGFR was abnormal, a binary logistic regression equation was constructed incorporating SWV, age, Scr, and Cys C. The resulting nomogram is shown in Figure 3. The ROC curves of the training set (AUC =0.962) and validation set (AUC =0.929) are shown in Figure 4A. According to the ROC curves, the multivariate model constructed with SWV, age, Scr, and Cys C demonstrated good performance (AUC =0.962) as compared to the univariate models (Figure 4B). Calibration curves for both the training cohort and validation cohort are shown in Figure 5. Since the P value derived from the validation set was <0.05, recalibration was implemented via the Platt scaling approach to optimize the model’s calibration ability. As shown in Figure 5C, the recalibrated curves exhibited favorable calibration efficiency. Decision curve analysis was performed to evaluate the clinical utility of the model, with the net benefit being calculated across a range of threshold probabilities. The model provided a higher net benefit than did the treat-all and treat-none strategies both in training set and validation set (Figure 6).

Figure 3 The nomogram based on a binary logistic regression equation incorporating SWV, age, Scr, and serum Cys C. Cys C, cystatin C; eGFR, estimated glomerular filtration rate; Scr, serum creatinine; SWV, shear wave velocity.

Figure 4 The ROC curve of the training and validation set (A) and the ROC curve of model comparison (B). (A) The AUC of the training set was 0.962 (95% CI: 0.944–0.980), and the AUC of validation set was 0.929 (95% CI: 0.888–0.971). (B) Comparison between the established model (AUC =0.962; 95% CI: 0.944–0.980) and individual variables. AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic; Scr, serum creatinine; SWV, shear wave velocity.

Figure 5 The calibration curves for the training cohort (A), the validation cohort (B), and the validation cohort after recalibration (C). Cys C, serum cystatin C.

Figure 6 The DCA of the training set (A) and validation set (B). DCA, decision curve analysis.

Discussion

The findings of this study demonstrated that the binary logistic regression model constructed with SWV, age, Scr, and Cys C provides relatively good performance in determining whether the indicators of renal allograft function are normal. In addition, SWV was found to be correlated with RTT, Scr, eGFR, BUN, UA, and Cys C, suggesting that SWV may reflect changes in renal function to a certain extent. These findings indicate that the combination of SWV and certain laboratory parameters may facilitate the clinical monitoring of renal allograft function.

A few studies have examined using SWE for the noninvasive assessment of renal allograft function. Maralescu et al. found that eGFR is positively correlated with cortical stiffness and viscosity; in their study, the cutoff value of renal cortical stiffness was <27.3 kPa for the detection of eGFR <60 mL/min/1.73 m², with a sensitivity of 80% and a specificity of 85% (AUC =0.811; P<0.0001) (9). However, Ghonge et al. and other researchers found that eGFR is negatively correlated with the stiffness of renal allografts (6,10,11), which is in line with our study. The use of calcineurin inhibitors (cyclosporin a and tacrolimus) can lead to tubulointerstitial damage, glomerulosclerosis, and luminal narrowing (12). Fibrosis, glomerulosclerosis, and tubular atrophy in renal allografts lead to the deterioration of renal function. In turn, this results in a decreased eGFR; elevated levels of Scr, BUN, UA, and Cys C; and increased elasticity values. Croci et al. reported that the stiffness is positively correlated with RTT (r=0.475; P<0.05) (13), which conflicts with our findings (ρ=–0.128; P<0.05). This discrepancy may be explained by differences in the baseline characteristics and clinical conditions of the enrolled patients, which include variability in post-transplantation intervals, the degree of interstitial fibrosis, and the hemodynamic status of the renal allograft. Given the weak correlation coefficient observed in our study, the relationship between SWV and RTT appears to be complex and affected by multiple factors in transplant recipients. Previous studies have reported there to be a significant correlation between renal allograft stiffness and the RI of segmental arteries in renal allografts (Pearson r=0.56; P=0.001) (6); however, the findings of our study indicated no significant association between SWV and the RI of renal allograft vessels at any hierarchical levels. We further found a significant difference in the RI of the segmental artery, the interlobar artery, and the arcuate artery of renal allograft between the normal function group and the abnormal function group. In a previous study, the RI of all renal allograft arteries was significantly higher in the delayed graft function group than in the normal allograft function group (14). It is important to note that while significant differences in RI were observed between the groups in our study, the correlation between SWV and RI was not significant. This finding may reflect the distinct physiological determinants of these two parameters. RI is a hemodynamic parameter predominantly influenced by vascular compliance, renal interstitial pressure, and downstream resistance (15). In contrast, SWV is a direct measure of tissue stiffness, primarily determined by the structural composition of the renal parenchyma, such as the degree of fibrosis or cellular infiltration (15,16). The pathological changes contributing to altered vascular impedance (and thus RI) may not occur synchronously or linearly with changes in parenchymal stiffness. Therefore, the lack of a direct correlation between SWV and RI, despite group-wise differences in RI, underscores that these two modalities provide complementary, rather than interchangeable, information about the transplanted kidney.

In our study, the difference between the SWV of the normal renal allograft function group and the SWV of the abnormal renal allograft function group was significant, which is in line with previous research (6,17,18). In our study, the cutoff value for determining whether the eGFR of renal allografts was normal was 3.15 m/s (AUC =0.645). Several factors may explain the relatively modest discriminative ability. The heterogeneity of the study population—including variability in potential confounders such as BMI, degrees of renal impairment, and pathological conditions (e.g., acute kidney injury, interstitial edema, and chronic fibrosis)—might have contributed to overlapping SWV values between the groups. Other studies have similarly reported moderate AUC values in the range of 0.649–0.755 for SWV in differentiating various states of allograft dysfunction (7,19). This variability across studies underscores the complexity of using a single elasticity parameter to capture the multifaceted nature of renal allograft pathology.

Multiple factors beyond renal function—such as hemodynamics, hydration status, rejection, inflammation, urinary obstruction, and immunosuppressive therapy—have been identified as contributors to renal stiffness (20-22). To ensure the reliability of our results, patients with renal allograft hemodynamic abnormalities (e.g., renal arteriovenous stenosis and anastomotic stenosis) and transplant hydronephrosis were strictly excluded according to the predefined inclusion and exclusion criteria. In addition, all participants were required to empty their bladders before the examination, which could effectively eliminate the potential interference from intravesical pressure. Previous studies have found that acute rejection, inflammatory lesions, and immunosuppressant toxicity—including corticosteroids and cyclosporine A, which can cause metabolic disturbances or renal damage—can also affect renal allograft stiffness (19,23). Pathological control data were not available in our study. We preliminarily applied this method to renal transplant recipients, but future implementation of the examination prior to renal allograft biopsy may provide more reliable evidence for clinical reference.

In line with previous research (7,24), laboratory parameters for renal allograft function, RI for hemodynamics, and SWV for graft stiffness were included in our study. In the univariate analysis, SWV, age, RTT, BUN, Scr, Cys C, UA, serum albumin, and the RI of segmental, interlobar artery, arcuate artery of the renal allograft were significantly correlated with the prediction of eGFR. In the multivariate analysis, SWV, age, Scr, and Cys C were significant indicators of renal allograft dysfunction. The model constructed based on these indicators achieved an AUC of 0.962, which was higher than the AUC values of each individual indicator. It demonstrated high AUC values in both the training and validation sets (training set: 0.962; validation set: 0.929). Moreover, the calibration curves for both the training and validation sets, along with the decision curves, demonstrate that the model provides good performance.

Scr reflects kidney function, and its AUC was 0.930. Cys C serves as a sensitive endogenous marker for evaluating glomerular filtration function (25), whose production is independent of muscle mass, gender, and age (26). It could also be a specific and valuable biomarker of fibrotic disease (27). The increase in Cys C expression level, primarily triggered by TGF-β1 during the myogenic differentiation of fibroblasts, appears to be a common feature of the fibrotic process (27). It can be used to monitor the recovery of renal function in patients after kidney transplantation (28), which allows for earlier and more sensitive detection of acute kidney injury than does Scr (29,30). Some studies indicate that glomerular filtration rate measured based on Cys C provides a more accurate reflection of renal function status (31,32). Previous research has demonstrated that Cys C can serve as an early predictive marker for diabetic nephropathy (33). Increased urinary levels of Cys C in renal transplant recipients are associated with interstitial fibrosis and tubular injury (34). Collectively, these findings indicate that Cys C is capable of monitoring both acute and chronic renal lesions (35).

However, few studies have focused on the combined application of SWE and Cys C for the assessment of renal allograft function. In our study, we constructed a combined model of SWV and Cys C for monitoring renal allograft function, which has not been reported previously. Previous studies have indicated that SWV and Cys C can reflect pathological structural changes in renal allografts (24,35), which serve as the basis for renal function. In our study, the model combining SWV, Cys C, Scr, and age exhibited good predictive performance, a finding consistent with this evidence. In contrast, the binary logistic regression model we established based on SWV, age, Scr, and Cys C exhibited good efficacy in predicting normal versus abnormal renal allograft function. Other work has reported that renal allograft stiffness is an important predictor of moderate-to-severe chronic changes in renal allografts, whereas age is not (24). Chronic lesions may affect the propagation of shear waves in renal allografts and can also be reflected in biological indicators of renal function. This is consistent with the results of our study. In the study by Kim et al. (7), which investigated the predictive value of elastography for subclinical and nonsubclinical rejection in renal allografts, age was a significant predictor in the univariate analysis but was not an independent predictor in the multivariate analysis. These results, together with those reported by Yang et al. (24), differed from the findings of our study. The discrepancy may be attributed to the fact that our study focused on the functional status of renal allografts as the primary endpoint, whereas the aforementioned studies adopted the presence of subclinical rejection or chronic changes in renal allografts as the endpoints. Further pathological verification and prospective studies should be conducted in the future. The duration of kidney transplantation was previously found to be an independent predictor of allograft injury (24), which is contrary to the findings of our study. The discrepancy may be due to the different outcome measures adopted: the previous work was based on pathological results of allograft biopsy, whereas our study evaluated allograft function via eGFR. Several studies have demonstrated that the histopathology of renal allografts gradually deteriorates with prolonged renal transplantation duration (36) and that transplantation duration constitutes an independent risk factor for chronic allograft lesions (24). Notably, the duration of renal transplantation failed to reach statistical significance in our multivariate analysis. A plausible explanation for this finding is that we adopted eGFR as the indicator for evaluating allograft functional changes, whereas the histopathological changes of the renal allograft were not captured by this assessment modality.

There are certain limitations to this study which should be addressed. First, histopathological findings from transplant kidney biopsies were not examined. Additionally, the established multivariate regression model did not undergo external validation with an independent cohort. Further prospective, multicenter studies with more pathologically confirmed cases are warranted to validate our model and improve the clinical applicability of our findings.

Conclusions

The nomogram established by incorporating SWV and other clinical factors (including age, Scr, and Cys C) demonstrated promising efficacy in predicting renal allograft function in our cohort. These preliminary findings suggest that the model may have potential as an adjunctive tool for supporting clinical assessment or as a risk stratification instrument for guiding follow-up strategies. However, given the single-center design and the lack of external validation and pathological correlation, these conclusions should be viewed as hypothesis-generating rather than as definitive. Further multicenter prospective studies are warranted to validate the model’s clinical utility.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0014/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0014/dss

Funding: This work was supported by the National Natural Science Foundation of China (No. 82102067) and Sichuan Science and Technology Program (No. 2024NSFSC0663).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0014/coif). All authors report that this work was supported by the National Natural Science Foundation of China (No. 82102067) and Sichuan Science and Technology Program (No. 2024NSFSC0663). The authors have no other 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. This study was approved by the Biomedical Ethics Review Committee of West China Hospital of Sichuan University. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All study subjects provided written informed consent prior to participation in the research.

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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