Evaluation of the Graft Kidney in the Early Postoperative Period: Performance of Contrast-Enhanced Ultrasound and Additional Ultrasound Parameters

Role of multi-parametric ultrasonography for the assessment and monitoring of functional status of renal allografts with histopathological correlation

BACKGROUND

The study focuses on the use of multi-parametric ultrasound [gray scale, color Doppler and shear wave elastography (SWE)] to differentiate stable renal allografts from acute graft dysfunction and to assess time-dependent changes in parenchymal stiffness, thereby assessing its use as an efficient monitoring tool for ongoing graft dysfunction. To date, biopsy is the gold standard for evaluation of acute graft dysfunction. However, because it is invasive, it carries certain risks and cannot be used for follow-up monitoring. SWE is a non-invasive imaging modality that identifies higher parenchymal stiffness values in cases of acute graft dysfunction compared to stable grafts.

AIM

To assess renal allograft parenchymal stiffness by SWE and to correlate its findings with functional status of the graft kidney.

METHODS

This prospective observational study included 71 renal allograft recipients. Multi-parametric ultrasound was performed on all patients, and biopsies were performed in cases of acute graft dysfunction. The study was performed for a period of 2 years at Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, a tertiary care center in north India. Independent samples t-test was used to compare the means between two independent groups. Paired-samples t-test was used to test the change in mean value between baseline and follow-up observations.

RESULTS

Thirty-one patients had experienced acute graft dysfunction at least once, followed by recovery, but none of them had a history of chronic renal allograft injury. Mean baseline parenchymal stiffness in stable grafts and acute graft dysfunction were 30.21 + 2.03 kPa (3.17 + 0.11 m/s) and 31.07 + 2.88 kPa (3.22 + 0.15 m/s), respectively; however, these differences were not statistically significant (P = 0.305 and 0.252, respectively). There was a gradual decrease in SWE values during the first 3 postoperative months, followed by an increase in SWE values up to one-year post-transplantation. Patients with biopsy-confirmed graft dysfunction showed higher SWE values compared to those with a negative biopsy. However, receiver operating characteristic analysis failed to show statistically significant cut-off values to differentiate between the stable graft and acute graft dysfunction.

CONCLUSION

Acute graft dysfunction displays higher parenchymal stiffness values compared to stable grafts. Therefore, SWE may be useful in monitoring the functional status of allografts to predict any ongoing dysfunction.

Application of ultrasound in early prediction of delayed graft function after renal transplantation

Kidney transplantation is currently the most effective treatment for end-stage renal disease. Delayed graft function (DGF) is one of the most common complications after renal transplantation and is a significant complication affecting graft function and the survival time of transplanted kidneys. Therefore, early diagnosis of DGF is crucial for guiding post-transplant care and improving long-term patient survival. This article will summarize the pathological basis and clinical characteristics of DGF after kidney transplantation, with a focus on contrast-enhanced ultrasound. It will analyze the current application status of ultrasound technology in DGF diagnosis and provide a comprehensive review of the clinical applications of ultrasound technology in this field, serving as a reference for the further application of ultrasound technology in kidney transplantation.

Predictive value of contrast-enhanced ultrasonography for the early diagnosis of renal dysfunction after kidney transplantation: A systematic review and meta-analysis

OBJECTIVES

We aimed to evaluate the changes in renal cortical microperfusion and quantitative contrast-enhanced ultrasonography (CEUS) parameters after kidney transplantation, and to determine the evidence-based value of CEUS in predicting renal dysfunction.

METHODS

The Embase, MEDLINE, Web of Science, and Cochrane Library databases were searched for relevant studies published from 2000 to 2023 on the use of CEUS to assess the renal cortical microcirculation after kidney transplantation. Subject terms and related keywords were combined, and a meta-analysis and systematic review were performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines.

RESULTS

The search yielded six studies involving 451 patients with moderate to high overall quality. The peak intensity (standardized mean difference [SMD]: -0.64, 95% confidence interval [CI] -1.13 to -0.15, p = 0.01) of CEUS was significantly lower in patients with renal dysfunction than in those with stable renal function. However, the time to peak (SMD: 0.28, 95% CI 0.04 to 0.52, p = 0.02) was significantly shorter in patients with renal dysfunction than in those with stable renal function. The total renal cortical microperfusion and renal cortical perfusion intensity were decreased, and the perfusion time was prolonged, in patients with renal dysfunction after kidney transplantation.

CONCLUSION

CEUS parameters can reflect real-time changes in renal cortical microperfusion, thus providing a basis for the early diagnosis of renal dysfunction after kidney transplantation.

Application value of ultrasonic contrast imaging and ultrasonic parameters in post-transplant renal surgery

Objective

Utilize VUEBOX quantitative analysis software to perform quantitative analysis dynamic ultrasound contrast images of post-transplant renal patients were assessed quantitatively five parameters of ultrasonic contrast and two-dimensional ultrasound are examined to explore their six value in Diagnosing Renal Graft Dysfunction.

Methods

A retrospective analysis was conducted on 73 post-transplant renal patients who underwent ultrasound contrast examinations at Yiyang Central Hospital from July 2022 to December 2023, They were diagnosed clinically and pathologically. Based on pathological and clinical diagnostic results, the patients were divided into three groups: 47 cases in the stable renal function group, 18 cases in the acute rejection (AR) group, and 8 cases in the delayed graft function (DGF) group. All patients underwent routine ultrasound and ultrasound contrast examinations post-transplantation. By comprehensively assessing renal function test results, clinical course, and pathological findings, differences in ultrasonic contrast quantitative parameters were analyzed. Additionally, ROC curves were constructed to evaluate the diagnostic efficacy of ultrasound contrast in discriminating between transplant renal rejection reactions and delayed renal function recovery.

Results

Statistically significant differences in characteristics, such as renal segmental artery resistance index, were observed among the stable renal function group, AR group, and DGF group (all P < 0.05), while peak systolic velocity showed no statistical significance (P > 0.05). Differences in cortical time to peak (TTP), medullary time to peak(TTP), main renal artery rise time (RT), main renal artery(TTP), and main renal artery fall time (FT) were statistically significant among the stable renal function group, AR group, and DGF group (P < 0.05). ROC curve analysis demonstrated that the accuracy of quantitative parameters for the DGF group and AR group was as follows: Renal artery TTP = Renal artery RT > Renal artery FT > Medulla TTP > Cortex TTP (with respective area under the curve values of 0.828, 0.828, 0.758, 0.742, 0.719). Among these, Renal artery TTP and Renal artery RT exhibited larger AUC values, with sensitivities of 87.5% each and specificities of 81.2 and 87.5%, respectively.

Conclusion

There are discernible differences in VUEBOX quantitative parameters between post-transplant AR and DGF cases, thereby providing imaging references for diagnosing of acute rejection and functional impairment following renal transplantation.

Using 1/2 Descending Time in CEUS to Identify Renal Allograft Rejection

RATIONALE AND OBJECTIVES

This study investigates the potential of quantitative Contrast-Enhanced Ultrasound (CEUS) parameters to distinguish between graft dysfunction due to rejection and non-rejection in kidney transplant recipients.

METHODS

In this retrospective study, 50 kidney transplant patients who presented elevated serum creatinine or proteinuria were analyzed. They were categorized as rejection or non-rejection based on biopsy outcomes. These classifications were applied in both derivation (n = 33) and validation cohorts (n = 17). Prior to the biopsy, all patients underwent a CEUS. Quantitative parameters derived from the CEUS were further analyzed for their consistency and reliability. Additionally, the relationship between the Banff scores, a standard for diagnosing transplant rejections, and these CEUS parameters was explored.

RESULTS

Significant differences between rejection and non-rejection groups were observed in the CEUS parameters of derivation cohorts. Specifically, Peak Intensity (PI), 1/2 Descending Time (DT/2), Area Under Curve (AUC), and Mean Transit Time (MTT) stood out. Sensitivity and specificity for these parameters were 76.5% and 87.5% for PI, 76.5% and 81.2% for DT/2, 76.5% and 87.5% for AUC, and 68.8% and 94.1% for MTT, respectively. DT/2 and MTT showed superior interobserver agreement compared to PI and AUC. When extrapolating the cutoff values from the derivation cohort to the validation group, DT/2 and AUC exhibited optimal diagnostic precision with positive and negative predictive values being 91.7% vs. 100% and 100% vs. 85.7%, respectively. Additionally, DT/2 effectively differentiated between mild and moderate to severe microvascular inflammation, pivotal in diagnosing antibody-mediated renal transplant rejection.

CONCLUSION

DT/2 from CEUS parameters presents as a reliable tool to differentiate rejection from non-rejection causes in renal transplant dysfunction. Yet, large-scale, multi-center studies are essential for further validation.

Ultrasound for monitoring different stages of post-transplant lymphoproliferative disorder in a transplanted kidney: A case report and review of the literature

RATIONALE

Post-transplant lymphoproliferative disorder (PTLD) is a well-recognized, but uncommon complication in patients with kidney transplantation, which poses challenges in diagnosis and poor prognosis due to its low incidence and nonspecific clinical manifestations. As a routine follow-up examination method for kidney transplant patients, ultrasound (US) plays a significant role in the diagnosis of PTLD. Therefore, it is critical to evaluate the ultrasonic characteristics of PTLD in transplanted kidney patients for early detection and diagnosis.

PATIENT CONCERNS

A 59-year-old female patient was unexpectedly found with a mass in the hilum of the transplanted kidney 12th month after transplantation, which gradually grew up in the following 4 months. The latest US examination found hydronephrosis. Contrast-enhanced ultrasound (CEUS) demonstrated a hypo-enhancement pattern in arterial and parenchymal phases and showed a new irregular area lacking perceivable intensification within the mass, which was considered necrosis. Meanwhile, the patient developed an acute increase in serum creatinine from 122 to 195 μmol/L.

DIAGNOSIS

A US-guided biopsy was conducted with the final pathological diagnosis of PTLD (polymorphic).

INTERVENTIONS

After receiving 3 times of rituximab and symptomatic treatment, blood creatinine returned to normal but the mass was still progressing in the patient. Therefore, the treatment approach was modified to immune-chemotherapy.

OUTCOMES

The patient was in a stable condition to date.

LESSONS

PTLD is a rare complication in a transplanted kidney. US and CEUS are the preferred imaging methods in renal transplant patients due to their good repeatability and no nephrotoxicity. This case demonstrates that continuous dynamic monitoring by using US and CEUS has significant value in the detection and diagnosis of PTLD in a transplanted kidney, suggesting early clinical intervention to avoid further progression.

Quantitative ultrasound for non-invasive evaluation of subclinical rejection in renal transplantation

OBJECTIVES

This study aimed to investigate the predictive efficacy of shear-wave elastography, superb microvascular imaging (SMI), and CEUS for allograft rejection in kidney transplants without graft dysfunction.

METHODS

From January 2021 to November 2021, 72 consecutive patients who underwent both allograft biopsy and ultrasound were evaluated. Blood test results were obtained within a week of the ultrasound examinations, which were performed before the protocol biopsy. Resistive index (RI), tissue viscoelasticity, vascular index, and quantitative CEUS parameters were measured. Patients were divided based on biopsy results into the rejection and non-rejection groups.

RESULTS

Among the 72 patients, 21 patients had pathological characteristics of acute rejection. RI of allograft was significantly higher in the rejection group (p = 0.007), compared to the non-rejection group. There were no significant between-group differences in vascular indices of SMI, mean elasticity, and mean viscosity. Meanwhile, among the parameters obtained by the time-intensity curve on CEUS, the cortical and medullary ratios of average contrast signal intensity, peak enhancement, wash-in area AUC, wash-in perfusion index, wash-out AUC, and wash-in and wash-out AUC were significantly different between the two groups (p < 0.05). In the receiver operating characteristic curve analysis for predicting allograft rejection, the AUC was 0.853 for the combination of six CEUS parameters, RI, and blood urea nitrogen.

CONCLUSIONS

Among non-invasive quantitative ultrasound measurements, CEUS parameters are the most useful for diagnosing subclinical allograft rejection. Furthermore, the combination of CEUS parameters, RI, and blood urea nitrogen may be helpful for the early detection of renal allograft rejection.

KEY POINTS

• Among non-invasive quantitative ultrasound measurements, CEUS parameters are the most useful for the diagnosis of subclinical allograft rejection. • On CEUS, the C/M ratios of MeanLin, PE, WiAUC, WiPI, WoAUC, and WiWoAUC are significantly lower in the rejection group; the combination of these showed reliable predictive performance for rejection. • The combination of CEUS parameters, RI, and BUN has a high predictive capability for subclinical allograft rejection.

Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features

Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.

Machine Learning-Based Ultrasound Radiomics for Evaluating the Function of Transplanted Kidneys

The aim of the study described here was to investigate the value of different machine learning models based on the clinical and radiomic features of 2-D ultrasound images to evaluate post-transplant renal function (pTRF). We included 233 patients who underwent ultrasound examination after renal transplantation and divided them into the normal pTRF group (group 1) and the abnormal pTRF group (group 2) based on their estimated glomerular filtration rates. The patients with abnormal pTRF were further subdivided into the non-severe renal function impairment group (group 2A) and the severe impairment group (group 2B). The radiomic features were extracted from the 2-D ultrasound images of each case. The clinical and ultrasound image features as well as radiomic features from the training set were selected, and then five machine learning algorithms were used to construct models for evaluating pTRF. Receiver operating characteristic curves were used to evaluate the discriminatory ability of each model. A total of 19 radiomic features and one clinical feature (age) were retained for discriminating group 1 from group 2. The area under the receiver operating characteristic curve (AUC) values of the models ranged from 0.788 to 0.839 in the test set, and no significant differences were found between the models (all p values >0.05). A total of 17 radiomic features and 1 ultrasound image feature (thickness) were retained for discriminating group 2A from group 2B. The AUC values of the models ranged from 0.689 to 0.772, and no significant differences were found between the models (all p values >0.05). Machine learning models based on clinical and ultrasound image features, as well as radiomics features, from 2-D ultrasound images can be used to evaluate pTRF.

Development and Validation of a Prognostic Nomogram for Prognosis in Patients With Renal Artery Stenosis

Background and Objective

Renal artery stenosis (RAS) is associated with an increased risk of renal function deterioration (RFD). Our previous study showed that renal cortical blood perfusion assessed by contrast-enhanced ultrasound (CEUS) was an important related factor for RFD in RAS patients. Based on several conventional related factors confirmed by previous studies, we aimed to establish and verify a CEUS+ scoring system to evaluate the risk of RFD at 1 year of follow-up in RAS patients.

Methods

This study was a single-center retrospective study. A total of 497 elderly RAS patients (247 in the training group and 250 in the verification group) admitted to the Beijing Hospital from January 2016 to December 2019 were included. The baseline characteristics of the patients on admission (including general conditions, previous medical history, blood pressure, blood creatinine, RAS, and cortical blood perfusion in the affected kidney) and renal function [glomerular filtration rate (GFR)] at 1-year of follow-up were collected. We used the univariate and multivariate logistic regressions to establish a CEUS+ scoring system model, the receiver operating characteristic (ROC) curve and area under the curve (AUC) to evaluate prediction accuracy, and the decision curve analysis and nomogram to evaluate the clinical application value of CEUS+ scoring system model.

Results

Among the 497 patients enrolled, 266 (53.5%) were men, with an average age of (51.7 ± 19.3) years. The baseline clinical-radiomic data of the training group and the verification group were similar (all p > 0.05). Multivariate logistic regression analysis results showed that age [Odds ratio (OR) = 1.937, 95% confidence interval (CI): 1.104–3.397), diabetes (OR = 1.402, 95% CI: 1.015–1.938), blood pressure (OR = 1.575, 95% CI: 1.138–2.182), RAS (OR = 1.771, 95% CI: 1.114–2.816), and area under ascending curve (AUCi) (OR = 2.131, 95% CI: 1.263–3.596) were related factors for the renal function deterioration after 1 year of follow-up (all p < 0.05). The AUC of the ROC curve of the CEUS+ scoring system model of the training group was 0.801, and the Youden index was 0.725 (specificity 0.768, sensitivity 0.813); the AUC of the ROC curve of the validation group was 0.853, Youden index was 0.718 (specificity 0.693, sensitivity 0.835). There was no significant difference in ROC curves between the two groups (D = 1.338, p = 0.325). In addition, the calibration charts of the training and verification groups showed that the calibration curve of the CEUS+ scoring system was close to the standard curve (p = 0.701, p = 0.823, both p > 0.10).

Conclusion

The CEUS+ scoring system model is helpful in predicting the risk of worsening renal function in elderly RAS patients.

Contrast-enhanced ultrasound of the kidneys: principles and potential applications

Contrast-enhanced ultrasound (CEUS) is an extension and an enhanced form of ultrasound that allows real-time evaluation of the various structures in different vascular phases. The last decade has witnessed a widespread expansion of CEUS applications beyond the liver. It has shown fair potential in kidneys and its diagnostic efficacy is comparable to CT and MRI. Ultrasound is the well-accepted screening modality for renal pathologies, however, it underperforms in the characterization of the renal masses. CEUS can be beneficial in such cases as it can help in the characterization of such incidental masses in the same sitting. It has an excellent safety profile with no risk of radiation or contract-related nephropathy. It can aid in the correct categorization of renal cysts into one of the Bosniak classes and has proven its worth especially in complex cysts or indeterminate renal masses (especially Bosniak Category IIF and III). Few studies also describe its potential role in solid masses and in differentiating benign from malignant masses. Other areas of interest include infections, infarctions, trauma, follow-up of local ablative procedures, and VUR. Through this review, the readers shall get an insight into the various applications of CEUS in kidneys, with imaging examples.

Clinical and Renal Cortical Blood Perfusion Characteristics in Patients with Severe Atherosclerotic Renal Artery Stenosis Who Underwent Stent Implantation: A Single-center Retrospective Cohort Study

Objective This study aimed to observe the clinical imaging features of patients with severe atherosclerotic renal artery stenosis (ARAS) receiving stent implantation, and to evaluate the associations between baseline clinical and imaging factors and renal-function deterioration at a 1-year follow-up.Methods This study was a single-center retrospective cohort study. A total of 159 patients with unilateral severe ARAS who underwent stent implantation at Beijing Hospital between July 2017 and December 2020 were consecutively enrolled. According to the renal glomerular filtration rate (GFR), detected by radionuclide renal imaging at 1-year follow-up, all patients were divided into a poor-prognosis group (with a ≥30% decrease in renal GFR; n=32 cases) and a control group (127 cases). Clinical imaging data, including the renal cortical blood perfusion pre- and post-sent implantation, were analyzed. Univariate and multivariate logistic regression analysis was used to evaluate the associations between clinical and imaging factors and renal-function deterioration.Results Of the 159 patients enrolled, 83 (52.2%) were men, with an average age of (57.2±14.7) years. The patient age, rate of diabetes, and systolic blood and diastolic blood pressure in the poor-prognosis group were significantly higher than those in the control group (all P<0.05). Before stent treatment, patients in the poor-prognosis group, compared with the control group, had a significantly smaller area under the ascending curve (AUC1), area under the descending curve (AUC2), and peak intensity (PI), and a longer time to peak intensity (TTP) and mean transit time (MTT) (all P<0.05). After stent treatment, patients in the poor-prognosis group, compared with the control group, showed significantly smaller AUC1, AUC2, and PI, and longer MTT (all P<0.05). Multivariate logistic regression analysis indicated that age (OR=1.251, 95%CI: 1.113–1.406, P=0.0002), diabetes (OR=1.472, 95%CI: 1.110–1.952, P=0.007), systolic blood pressure (OR=1.339, 95%CI: 1.082–1.657, P=0.007), renal GFR (OR=2.025, 95%CI: 1.217–3.369, P=0.006), and AUC1 post-stent (OR=2.173, 95%CI: 1.148–4.113, P=0.017) were the factors associated with renal deterioration at the 1-year follow-up.Conclusions Patients with severe RAS with renal-function deterioration after stent implantation were older, and often had diabetes, hypertension, and impaired renal cortical perfusion. Age, diabetes, systolic blood pressure, renal GFR, and AUC1 after stent implantation were independent factors associated with short-term renal deterioration.

Quantitative contrast-enhanced ultrasound for the differentiation of kidney allografts with significant histopathological injury

OBJECTIVE

To identify specific quantitative contrast-enhanced ultrasound (CEUS) parameters that could distinguish kidney transplants with significant histopathological injury.

METHODS

Sixty-four patients were enrolled in this prospective observational study. Biopsies were performed following CEUS and blood examination.

RESULTS

28 biopsy specimens had minimal changes (MC group), while 36 had significant injury (SI group). Of these, 12 had rejection (RI group) and 24 non-rejection injury (NRI group). In RI and NRI groups, temporal difference in time to peak (TTP) between medulla and cortex (ΔTTPm-c) was significantly shorter compared to the MC group (5.77, 5.92, and 7.94 s, P = 0.048 and 0.026, respectively). Additionally, RI group had significantly shorter medullary TTP compared to the MC group (27.75 vs. 32.26 s; P = 0.03). In a subset of 41 patients with protocol biopsy at 1-year post-transplant, ΔTTPm-c was significantly shorter in the SI compared to the MC group (5.67 vs. 7.67 s; P = 0.024). Area under receiver operating characteristic curves (AUROCs) for ΔTTPm-c was 0.69 in all patients and 0.71 in patients with protocol biopsy.

CONCLUSIONS

RI and NRI groups had shorter ΔTTPm-c compared to the MC group. AUROCs for both patient groups were good, making ΔTTPm-c a promising CEUS parameter for distinguishing patients with significant histopathological injury.