Application value of shear-wave elastography combined with monochrome superb microvascular imaging in renal allograft chronic rejection

Abstract

BACKGROUND:

Conventional ultrasound (US), which include gray scale US and Doppler US, is the first-line imaging modality for the evaluation of renal allograft; however, conventional US indicators have limitations.

OBJECTIVE:

To explore the application value of shear-wave elastography (SWE) combined with monochrome superb microvascular imaging (mSMI) in renal allograft chronic rejection (CR).

METHODS:

From November 2021 to February 2022 in the Lanzhou University Second Hospital, the US features of 54 patients with renal allograft were retrospectively analyzed. Patients were categorized into two groups: stable group(n = 44) and CR group(n = 10), with clinical diagnosis as reference standard. The vascular index (VI) on mSMI and parenchymal stiffness were measured in the middle cortex of all renal allografts and receiver operating characteristic (ROC) curves were drawn to evaluate the feasibility of differentiation. Statistically significant US features and biochemical indicators such as creatinine were scored, and the results of the scores were analyzed by ROC curve.

RESULTS:

The VI on mSMI of the stable group (49.5±2.0) was significantly greater than that of the CR group (33.8±5.9) (P = 0.028). There was a statistically significant difference in parenchymal stiffness between stable group (16.2kPa±1.2) and CR group (33.9kPa±6.6) (P = 0.027). The sensitivity was 90% and specificity was 81.8% of the scores in the differentiation of stable group from CR group (cut-off value, 2; P = 0.000).

CONCLUSION:

SWE combined with mSMI may help differentiate stable renal allograft from renal allograft CR and have the potential application value in the diagnosis of renal allograft CR.

Keywords

Shear-wave elastography monochrome superb microvascular imaging ultrasound;renal allograft chronic rejection

1 Introduction

Renal allograft chronic rejection (CR) is the most common cause of graft failure and is characterized by interstitial fibrosis and tubular atrophy [1 –4]. Clinically, suspicion of renal allograft CR is often dependent upon biochemical indicators such as creatinine. However, elevated creatinine may only manifest after organ injury and is neither sensitive nor specific [5]. The gold standard for diagnosis requires an invasive biopsy which can carry serious clinical risks including bleeding and graft loss as well as the possibility of sampling error [6]. The use of noninvasive imaging modalities to monitor renal allograft is of great clinical value, particularly as a tool for early detection of CR. Contrast-enhanced ultrasound (CEUS) can facilitate the evaluation of the blood distribution and supply perfusion information, which may be useful for diagnosis and differential diagnosis [7 –10]. However, CEUS is an invasive manipulation, and the administration of contrast agent requires extra cost and expertise of doctors [11, 12]. Shear-wave elastography (SWE) and monochrome superb microvascular imaging (mSMI) are noninvasive imaging techniques that provide information about renal parenchymal stiffness and microvasculature [13]. Therefore, the aim of our study was to explore the application value of SWE combined with mSMI in renal allograft CR.

2 Materials and methods

2.1 Patients

From November 2021 to February 2022 in the Lanzhou University Second Hospital, all patients with renal allograft were consecutively recruited. There were no specific inclusion criteria. The exclusion criteria and categorization method were illustrated in Flow diagram (Fig. 1). Finally, 54 patients, including 40 males and 14 females, aged (40.2±9.6) years, ranging from 24 to 57 years, were enrolled in this study and were categorized into two groups: stable group and CR group, with clinical diagnosis as reference standard. Renal allograft CR was defined as the progressive worsening of renal function for more than 3 months without recovery (estimated glomerular filtration rate <50 mL/min, increased creatinine level, proteinuria).

Fig. 1

Flow diagram illustrates exclusion criteria and categorization method adopted in this study. OPD = outpatient department, IPD = inpatient department, CR = chronic rejection.

Because this study was retrospective, the ethics committee of Lanzhou University Second Hospital waived the requirement for informed consent.

2.2 Methods

Conventional ultrasound (US), which included gray scale US and color Doppler flow imaging (CDFI), SWE, and mSMI images were obtained using color Doppler ultrasound diagnostic instrument APLIO I800(Canon, Japan), with convex array probe i8CX1(1–8 MHz) and linear array probe i18LX5(5–18 MHz). US examination was performed with the patients lying in a supine position and the patients were instructed to hold their breath when SWE and mSMI were performed. The US images were collected by two doctors with more than 5 years’ experience in renal allograft US diagnosis. All measurements and interpretations of the US images were performed by the same observer who was blinded to the patient data.

2.3 Features

The general information of the patients such as sex, age, body mass index (BMI), donor type, location of allograft, time on dialysis, post-transplantation time, and biochemical indicators were routinely recorded. The US features such as cortex and medulla boundary, cortical thickness, width/thickness ratio, resistance index (RI) of renal artery, mSMI in the middle cortex, three-dimensional advanced dramatic flow imaging(3D-ADF) in the middle cortex were observed and recorded (Figs. 2, 3A–C). With the built-in software of US machine, the VI on mSMI was automatically generated after the region of interest (ROI) placed in the middle cortex (Figs. 2, 3D). The rectangular ROI (0.75cm², 10860pixels) was positioned in the middle cortex of the renal allograft. A total of six measurements of parenchymal stiffness in the middle cortex were performed and recorded (Figs. 2, 3E). The circular ROI (diameter: 3 mm) was positioned in the middle cortex of the renal allograft excluding the medulla. The mean value of parenchymal stiffness was included for each patient.

Fig. 2

A 31-year-old man who underwent renal transplantation on September 19,2018 with stable allograft. (A). Gray scale ultrasound shows clear cortex and medulla boundary and the middle cortical thickness is 0.68 cm (linear array probe:5–18 MHz); (B). mSMI shows dense arcuate arteries and branches in the middle cortex (convex array probe:1–8 MHz); (C) 3D-ADF show dense arcuate arteries and branches in the middle cortex (convex array probe:1–8 MHz); (D) The VI on mSMI measured in the middle cortex was 50.0(linear array probe:5–18 MHz); (E) The parenchymal stiffness measured in the middle cortex was 18.7kPa (convex array probe:1–8 MHz). mSMI = monochrome superb microvascular imaging, 3D-ADF = three-dimensional advanced dramatic flow imaging, VI = vascular index.

Fig. 3

A 32-year-old man who underwent renal transplantation on July 27,2021 with renal allograft CR. (A). Gray scale ultrasound shows unclear cortex and medulla boundary and the middle cortical thickness is 0.60 cm (linear array probe:5–18 MHz); (B). mSMI shows slightly dense arcuate arteries and branches in the middle cortex (convex array probe:1–8 MHz); (C) 3D-ADF show slightly sparse arcuate arteries and branches in the middle cortex (convex array probe:1–8 MHz); (D) The vascular index on mSMI measured in the middle cortex was 16.7(linear array probe:5–18 MHz); (E) The parenchymal stiffness measured in the middle cortex was 14.4 kPa (convex array probe:1–8 MHz). mSMI = monochrome superb microvascular imaging, 3D-ADF = three-dimensional advanced dramatic flow imaging, VI = vascular index.

2.4 Statistical analysis

SPSS26.0 statistical software was used for data analysis. The quantitative data were expressed as mean±standard deviation and were compared using independent sample t test or approximate t test. The counting data were compared using Fisher’s exact test. Receiver operating characteristic (ROC) curves were drawn to evaluate the diagnostic performance and cut-off values were decided using the Youden index. P < 0.05 was considered a statistically significant difference.

3 Results

3.1 General information of the patients in two groups

Among 54 patients, 44 patients were with stable renal allograft and 10 patients were with renal allograft CR. As is shown in Table 1, there were statistically significant differences in biochemical indicators such as urea and creatinine in two groups, but there were no significant differences in sex, age, BMI, donor type, location of allograft, time on dialysis, and post-transplantation time.

Table 1
General information

Features Stable group(n = 44) CR group(n = 10) P value

Sex 0.708

Male/ Female, n 33/11 7/3

Age (year) 40.2±1.4 40.2±3.5 0.994

BMI (kg/m²) 21.9±0.6 23.6±1.3 0.279

Donor type 0.326

Living-related/Cadaveric, n 8/36 0/10

Location of allograft 0.601

Left/ Right iliac fossa, n 5/39 2/8

Time on dialysis (year) 4.9±0.8 8.3±2.5 0.230

Post-transplantation time (month) 34.2±3.9 35.3±11.0 0.914

Biochemical indicators

Urea (mmol/L) 8.0±0.5 12.1±1.7 0.036

Creatinine (μmol/L) 109.3±4.1 196.2±26.7 0.010

Uric acid (μmol/L) 358.8±11.5 371.2±22.6 0.641

With hypertension 18 5 0.728

Features	Stable group(n = 44)	CR group(n = 10)	P value
Sex			0.708
Male/ Female, n	33/11	7/3
Age (year)	40.2±1.4	40.2±3.5	0.994
BMI (kg/m²)	21.9±0.6	23.6±1.3	0.279
Donor type	0.326
Living-related/Cadaveric, n	8/36	0/10
Location of allograft	0.601
Left/ Right iliac fossa, n	5/39	2/8
Time on dialysis (year)	4.9±0.8	8.3±2.5	0.230
Post-transplantation time (month)	34.2±3.9	35.3±11.0	0.914
Biochemical indicators
Urea (mmol/L)	8.0±0.5	12.1±1.7	0.036
Creatinine (μmol/L)	109.3±4.1	196.2±26.7	0.010
Uric acid (μmol/L)	358.8±11.5	371.2±22.6	0.641
With hypertension	18	5	0.728

CR = chronic rejection, BMI = body mass index.

3.2 Differences in US features in two groups

As shown in Table 2, there were statistically significant differences in cortex and medulla boundary, parenchymal stiffness in two groups, but there were no significant differences in cortical thickness, width/thickness ratio, RI of main renal artery (MRA), segmental renal artery (SRA), and interlobar renal artery (IRA). There were statistically significant differences in mSMI of arcuate artery and its branches display both by convex array probe and linear array probe in two groups, but there were no significant differences in 3D-ADF of arcuate artery and its branches display.

Table 2
Differences in conventional ultrasound features in two groups

US Features Stable group(n = 44) CR group(n = 10) P value

Cortex and medulla boundary 0.020

Clear/Unclear, n 35/9 4/6

Cortical thickness(cm) 0.652±0.018 0.625±0.056 0.564

Width/Thickness ratio 0.152

≥1, n 42 8

RI

MRA 0.661±0.010 0.658±0.023 0.887

SRA 0.611±0.011 0.621±0.021 0.705

IRA 0.594±0.012 0.621±0.030 0.341

Arcuate artery and its branches display

Clear/Unclear on mSMI 42/2 6/4 0.008

Clear/Unclear on 3D-ADF 26/18 4/6 0.311

VI of mSMI 49.5±2.0 33.8±5.9 0.028

Parenchymal stiffness(kPa) 16.2±1.2 33.9±6.6 0.027

US Features	Stable group(n = 44)	CR group(n = 10)	P value
Cortex and medulla boundary			0.020
Clear/Unclear, n	35/9	4/6
Cortical thickness(cm)	0.652±0.018	0.625±0.056	0.564
Width/Thickness ratio	0.152
≥1, n	42	8
RI
MRA	0.661±0.010	0.658±0.023	0.887
SRA	0.611±0.011	0.621±0.021	0.705
IRA	0.594±0.012	0.621±0.030	0.341
Arcuate artery and its branches display
Clear/Unclear on mSMI	42/2	6/4	0.008
Clear/Unclear on 3D-ADF	26/18	4/6	0.311
VI of mSMI	49.5±2.0	33.8±5.9	0.028
Parenchymal stiffness(kPa)	16.2±1.2	33.9±6.6	0.027

US = ultrasound, CR = chronic rejection, RI = resistance index, MRA = main renal artery, SRA = segmental renal artery, IRA = interlobar renal artery, 3D-ADF = three-dimensional advanced dramatic flow imaging, mSMI = monochrome superb microvascular imaging, VI = vascular index.

3.3 Comparison of diagnostic performance of US features and biochemical indicators in the differentiation of stable renal allograft from renal allograft CR

The quantitative data of statistically and clinically significant were analyzed by ROC curve (Fig. 4A–C) and the best cut-off values were determined. The cut-off values for parenchymal stiffness, VI, and creatinine were defined as 27.35 (P = 0.013), 49.4 (P = 0.009), and 178.85 (P = 0.001), respectively. The detailed ROC analysis results are summarized in Table 4 and the ROC curves are shown in Fig. 4(A–C). Then the quantitative data were scored combing with the counting data of statistically and clinically significant: unclear cortex and medulla boundary, parenchymal stiffness ≥27.35kPa, VI ≤49.4, and creatinine ≥178.85μmol/L were respectively scored as 1 point, totaling 4 points (Table 3) and the results of the scores were analyzed by ROC curve (Fig. 4D). The sensitivity was 90.0% and specificity was 81.8% of the scores in the differentiation of stable group from CR group (cut-off value, 2; P = 0.000) (Table 4).

Fig. 4

The ROC curve of different features in the differentiation of stable group from CR group. (A). Parenchymal stiffness; (B). Vascular index; (C) Creatinine; (D) Score. ROC = receiver operating characteristic, CR = chronic rejection.

Table 3

The results of the scores combing US features and biochemical indicators

Score	Stable group(n = 44)	CR group(n = 10)	Total(n = 54)
0	23	0	23
1	13	1	14
2	7	2	9
3	1	5	6
4	0	2	2

US = ultrasound, CR = chronic rejection.

Table 4

Comparison of diagnostic performance of different features

Features	Cut-off	sensitivity	specificity	AUC	95% CI	P value
Parenchymal stiffness	27.35	70.0%	90.9%	0.755	0.539–0.970	0.013
VI	49.4	61.4%	80.0%	0.767	0.618–0.916	0.009
Creatinine	178.85	70.0%	100%	0.825	0.626–1.000	0.001
Score	2	90%	81.8%	0.859	0.731–0.987	0.000

AUC = area under the curve, 95% CI = 95 % confidence interval, VI = vascular index.

4 Discussion

SWE and mSMI are noninvasive imaging techniques that provide information about renal parenchymal stiffness and microvasculature. SWE, as a more recent technology offer either an average shear wave velocity within the tissue of ROI or an image, can be used to depict relative tissue stiffness or displacement (strain) in response to an imparted force [14, 15]. Several recent studies have reported the results of SWE in evaluating renal allograft [16, 17]. In our study, parenchymal stiffness was 16.2kPa±1.2 in patients with stable renal allograft and 33.9kPa±6.6 in patients with renal allograft CR (P = 0.027). We found a sensitivity of 70.0% and specificity of 90.9% for the differentiation of patients with stable renal allograft from those with renal allograft CR (cut-off value, 27.35kPa). Ghonge et al. [16] found the parenchymal stiffness in stable renal allograft and renal allograft CR were 8.51kPa±2.44 and 24.50 kPa±4.49. Using of the cut-off value of 15.695kPa resulted in a sensitivity and specificity of 100% for the differentiation of stable renal allograft from renal allograft CR.

As a recently developed US imaging modality, SMI has been available for use in routine examinations since 2014 [18]. Compared with traditional blood flow imaging such as CDFI, which can only show the blood vessels with high velocity and diameter >0.2 mm, SMI can visualize the small blood vessels with low velocity and diameter >0.1 mm without injection of contrast agent. SMI technology includes color imaging mode (cSMI, color superb microvascular imaging) and gray scale imaging mode (mSMI, monochrome superb microvascular imaging). mSMI is based on cSMI to suppress two-dimensional information and perform vascular imaging superposition. The VI is a new doppler parameter that is automatically determined by mSMI to quantify flow signals as the ratio of color pixels to all pixels. In this study, the VI on mSMI of the stable group (49.5±2.0) was significantly greater than that of the CR group (33.8±5.9) (P = 0.028). We found a sensitivity of 61.4% and specificity of 80.0% for the differentiation of patients with stable renal allograft from those with renal allograft CR (cut-off value, 49.4). There were statistically significant differences in mSMI of arcuate artery and its branches display both by convex array probe and linear array probe in two groups. Recently, a few studies on using SMI to display microvasculature in inflammatory soft tissue [19], in small lesions of hepatocellular carcinoma [20, 21], breast cancer [22], thyroid [23], skeletal muscle [24], and carotid plaques [25] have been reported. In a previous study, Marticorena Garcia et al. [26] found that cSMI of the cortical microvasculature consistently showed a qualitative decrease in cortical microvascular flow in renal allograft CR compared to stable renal allograft. However, as far as we know, there is no research about the VI on SMI in renal allograft CR. Our study found a quantitative decrease of cortical microvascular flow in CR group (33.8±5.9) compared to stable group (49.5±2.0), which is consistent with the previous qualitative research. Because CR leads to a deterioration of renal allograft function, the decrease of VI is also plausible from a clinical point of view. When CR occurs, the blood flow perfusion of renal cortex small vessels is affected, which is characterized by inflammation, cellulose necrosis or renal parenchyma fibrosis, and the renal cortex small vessels are necrotic or occluded, thus affecting the micro blood flow.

In our study, the diagnostic performance of parenchymal stiffness, VI, and creatinine were analyzed by ROC curve and then scored combing with the counting data of statistically and clinically significant: unclear cortex and medulla boundary, parenchymal stiffness ≥27.35 kPa, VI ≤49.4, and creatinine ≥178.85μmol/L were respectively scored as 1 point, totaling 4 points and the results of the scores were analyzed by ROC curve. The sensitivity was 90.0% and specificity was 81.8% of the scores in the differentiation of stable group from CR group (cut-off value, 2; P = 0.000), which means there is the possibility of CR when any two or more of the above four features appear, and further invasive biopsy may be needed to clarify. This suggests SWV and mSMI may be useful in early identification of patients with CR and may facilitate selection of patients for renal allograft biopsy. However, there are still some limitations of SWV and mSMI, including a lack of clinical standards and may be influenced by many factors. Hence, more studies are needed. As the technologies mature, SWV and mSMI may be useful in early identification of patients with renal allograft CR and may facilitate selection of patients for invasive diagnostic procedures such as allograft biopsy.

In addition, a few studies on using CEUS to analyzed quantitatively [27 –30] and evaluate renal allograft, such as vascular complications [7, 31], delayed allograft function [32], and allograft rejection [33, 34], have been reported recently. Although the CEUS demands special analysis software and a more expensive contrast agent, its general advantages and potential applications in renal allograft are notable. We will also conduct related research in the later period.

The limitations of this study include that it is a single-center study, with a relatively small sample size, which may have some influence on the research results, and further multi-center cooperation can be carried out in the later period; moreover, it is a retrospective study, and the US examinations were performed by two doctors, so there may be biases and interobserver variations.

5 Conclusion

In a word, SWE combined with mSMI may help differentiate stable renal allograft from renal allograft CR and have the potential application value in the diagnosis of renal allograft CR.

Footnotes

Acknowledgments

The authors thank all team members in the Medical Center of Ultrasound, Lanzhou University Second Hospital for their helpful cooperation.

Conflict of interest

The authors declare that they have no competing interest.

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