Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging

Abstract

Purpose

To investigate the potential of histogram analysis based on diffusion kurtosis imaging (DKI) in evaluating renal function and fibrosis associated with chronic kidney disease (CKD).

Materials and methods

Thirty-six CKD patients were enrolled, and DKI was performed in all patients before the renal biopsy. The histogram parameters of diffusivity (D) and kurtosis (K) were obtained using FireVoxel. The histogram parameters between the stable [estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m²] and impaired (eGFR < 60 ml/min/1.73 m²) eGFR group were compared. Besides, patients were classified into mild, moderate, and severe fibrosis group using a semi-quantitative standard. The correlations of histogram parameters with eGFR and fibrosis scores were investigated and the diagnostic performances of histogram parameters in assessing renal dysfunction and fibrosis were analyzed. The added value of combination of most significant parameter with 24 h urinary protein (24 h-UPRO) in evaluating fibrosis was also explored.

Results

Seven D histogram parameters in cortex (mean, median, 10th, 25th, 75th, 90th percentiles and entropy), two D histogram parameters in medulla (75th, 90th percentiles), seven K histogram parameters in cortex (mean, min, median, 10th, 25th, 75th, 90th percentiles) and three K histogram parameters in medulla (mean, median, 25th percentile) were significantly different between the two groups. The D_mean of cortex was the most relevant parameter to eGFR (r = 0.648, P < 0.001) and had the largest area under the curve (AUC) for differentiating the stable from impaired eGFR group [AUC = 0.889; 95% confidence interval (CI) 0.728–0.970]. The K_90th of cortex presented the strongest correlation with fibrosis scores (r = 0.575, P < 0.001) and achieved the largest AUC for distinguishing the mild from moderate to severe fibrosis group (AUC = 0.849, 95% CI 0.706–0.993). Combining the K_90th in cortex with 24 h-UPRO gained statistically higher AUC value (AUC = 0.880, 95% CI 0.763–0.996).

Conclusion

Histogram analysis based on DKI is practicable for the noninvasive assessment of renal function and fibrosis in CKD patients.

Introduction

Chronic kidney disease (CKD) shows gradually increased morbidity and mortality recently, thus becoming a worldwide public health problem [1]. Progressive decline of renal function may reach an endpoint of end-stage renal failure, which will make the patients undertake a high risk of death. Furthermore, renal fibrosis has consistently been shown to be the best predictor of progression in CKD [2]. Thus, timely and regularly monitoring of renal dysfunction and fibrosis is essential for guiding therapy and preventing the patients from poor prognosis [3, 4].

At present, the most common method for evaluating renal function is the estimated glomerular filtration rate (eGFR) based on serum creatinine (SCr) [5]. However, SCr is susceptible to various factors, which causes the restriction in the sensitivity of assessing renal function [6]. Renal biopsy is currently the gold standard of identifying the presence and extent of fibrosis, but it’s invasive, with the risk of severe complications, and should not be used for regular follow-up [7]. Hence, noninvasive and accurate techniques for the assessment of renal function and pathological progression are needed.

Diffusion-weighted imaging (DWI) is a noninvasive magnetic resonance imaging (MRI) technology that provides information about the movement of water molecules and is quantified by the apparent diffusion coefficient (ADC) [8]. Prior studies have demonstrated the potential of DWI to monitor renal function and pathological alteration noninvasively [9,10,11]. Conventional DWI follows a simple mono-exponential pattern based on Gaussian diffusion behavior without restriction [12]. However, water diffusion in living tissues is more complicated and is always restricted due to the presence of microstructures, such as cell membranes and organelles and extracellular matrix (ECM) in the fibrotic kidney tissue, namely non-Gaussian phenomena [13]. Thus, non-Gaussian model diffusion kurtosis imaging (DKI) was developed to provide greater sensitivity in tissue with microstructural complexity [13, 14]. This model evaluates the kurtosis (K) coefficient, which shows the deviation of tissue diffusion from a Gaussian approach, and the diffusivity (D) coefficient with the correction of non-Gaussian bias [15]. DKI may provide additional information and has shown promising performance in evaluating the alterations of renal function and assessing the degree of renal pathological injury of CKD in previous researches [16,17,18].

However, routine DKI signal measurements only provide mean values, which do not account for the underlying spatial distribution. Histogram analysis refers to the application of mathematical methods to analyze the relationship and distribution of pixel or voxel gray levels in the image, which reflects histologic characteristics and heterogeneity [19,

Abbreviations

AUC:

Area under the curve

ADC:

Apparent diffusion coefficient

BUN:

Blood urea nitrogen

CKD:

Chronic kidney disease

CKD-EPI:

Chronic kidney disease epidemiology collaboration

CI:

Confidence interval

D:

Diffusivity

DWI:

Diffusion-weighted imaging

DKI:

Diffusion kurtosis imaging

eGFR:

Estimated glomerular filtration rate

ECM:

Extracellular matrix

FOV:

Field of view

ICC:

Intraclass correlation coefficient

K:

Kurtosis

MRI:

Magnetic resonance imaging

ROC:

Receiver-operating characteristic

ROI:

Region of interest

SCr:

Serum creatinine

SS-EPI:

Single-shot echo planar

SLEEK:

Spatial labeling with multiple inversion pulses

T2WI:

T2-weighted imaging

T1W:

T1-weighted imaging:

TR:

Repetition time

TE:

Echo time

24 h-UPRO:

24 h urinary protein