Non-invasive evaluation of the pathological and functional characteristics of chronic kidney disease by diffusion kurtosis imaging and intravoxel incoherent motion imaging: comparison with conventional DWI

Delta corticomedullary apparent diffusion coefficient on MRI as a biomarker for prognosis in IgA nephropathy

OBJECTIVES

To explore the association of the cortico-medullary difference in apparent diffusion coefficient (ΔADC) with clinicopathological parameters of disease activity at the time of biopsy, and with the prognositic risk stratification in IgA nephropathy (IgAN) patients.

METHODS

We included 112 patients with biopsy-proven IgAN who measured ΔADC. Patients underwent a kidney biopsy and diffusion-weighted magnetic resonance imaging within one week of the biopsy. Clinicopathological characteristics were compared according to different ΔADC levels. The effect of ΔADC on eGFR and kidney fibrosis was explored using multivariate regression and ROC analysis. An individual's 5-year risk probability of progressing to ESKD or decreasing of eGFR > 50% was calculated by the guidelines-recommended international risk-prediction tool in IgAN. The effect of ΔADC on prognostic risk stratification was assessed. Net reclassification improvement (NRI) was used to evaluate the model performance.

RESULTS

The average ΔADC was 168.89 ± 85.1 x10-6 mm2/s. ΔADC levels decreased significantly with increasing chronic kidney disease (CKD) stages (p = 0.0038). Spearman correlation analysis revealed that ΔADC was positively correlated with eGFR, hemoglobin, serum albumin, while negatively correlated with levels of serum creatine (Scr), blood urea nitrogen (BUN), T score of Oxford classification and Lee grades (p < 0.05). Moreover, we showed that ΔADC was independently associated with eGFR (β = 0.04, 95% CI = [0.003, 0.077], p = 0.033) demonstrated by a backward stepwise multivariate linear regression analysis. Besides, ΔADC, a combination of ΔADC and eGFR showed an AUC of 0.776 (60% sensitivity and 85.3% specificity) and an AUC of 0.875 (100% sensitivity and 69.6% specificity) respectively for evaluating kidney interstitial fibrosis (IF) severity. Furthermore, ΔADC showed an AUC of 0.792 (95% CI 0.677-0.906) for differentiating higher progression risk categories from lower categories (specificity = 91.6%, sensitivity = 58.8%). The low-ΔADC group (≤ median value 167.1 × 10-6 mm2/s) was associated with 7.509-fold higher likelihood of higher progression risk compared to the high-ΔADC group (>167.1 × 10-6 mm2/s) in a fully-adjusted model. And reclassification analyses confirmed that the final adjusted model improved NRI.

CONCLUSIONS

ΔADC was significantly associated with kidney function and enabled a reliable evaluation of kidney IF severity in IgAN patients. Low ΔADC can predict a high 5-year kidney progression risk in IgAN, independent of important clinical factors. Moreover, the predictive ability to identify patients at high risk of severe kidney fibrosis and adverse progression estimates with satisfactory accuracy, facilitating ΔADC a promising and noninvasive tool in complementarily evaluating disease activity and the prognostic risk stratification in patients with IgAN.

Multiparametric MRI: can we assess renal function differently?

We are lacking tools to evaluate renal performance. In this review, we presented the current knowledge and potential future applications in nephrology of new magnetic resonance imaging (MRI) techniques, focusing on diffusion-weighted (DWI) MRI, blood oxygen level-dependent (BOLD) MRI, and magnetic resonance relaxometry (T1 and T2 mapping). These sequences are sensitive to early changes in biological processes such as perfusion, oxygenation, edema, or fibrosis without requiring contrast medium injection and avoids irradiation and nephrotoxicity. Combining these different sequences into the so-called "multiparametric MRI" enables noninvasive, repeated exploration of renal performance on each kidney separately. DWI MRI, which evaluates the movement of water molecules, is a promising tool for noninvasive assessment of interstitial fibrosis and the cortical restricted diffusion has a prognostic value for the deterioration of renal function in diabetic nephropathy. BOLD MRI is sensitive to changes in renal tissue oxygenation based on the paramagnetic properties of deoxyhemoglobin and is of particular interest in the setting of renal artery stenosis to assess tissue oxygenation in the post-stenotic kidney. This sequence can be used for predicting degradation of renal function in chronic kidney diseases (CKD) and might be useful in preclinical studies to assess nephroprotective and nephrotoxic effects of drugs in development. T1 and T2 relaxation times change with tissue water content and might help assessing renal fibrosis. A corticomedullary dedifferentiation in T1 has been observed in CKD and negatively correlates with glomerular filtration rate. Data on the significance of T2 values in renal imaging is more limited. Multiparametric MRI has the potential to provide a better understanding of renal physiology and pathophysiology, a better characterization of renal lesions, an earlier and more sensitive detection of renal disease, and an aid to personalized patient-centered therapeutic decision-making. Further data and clinical trials are needed to allow its routine application in clinical practice.

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model

BACKGROUND

Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier.

OBJECTIVE

To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach.

METHODS

Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading.

RESULTS

All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90).

CONCLUSION

The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models

OBJECTIVES

To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD).

METHODS

A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models.

RESULTS

All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features.

CONCLUSIONS

Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD.

ADVANCES IN KNOWLEDGE

Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

MR Perfusion Imaging for Kidney Disease

Renal perfusion reflects overall function of a kidney. As an important indicator of kidney diseases, renal perfusion can be noninvasively measured by multiple methods of MR imaging, such as dynamic contrast-enhanced MR imaging, intravoxel incoherent motion analysis, and arterial spin labeling method. In this article we introduce the principle of the methods, review their recent technical improvements, and then focus on summarizing recent applications of the methods in assessing various renal diseases. By this review, we demonstrate the capability and clinical potential of the imaging methods, with the hope of accelerating their adoption to clinical practice.

Based on functional and histopathological correlations: is diffusion kurtosis imaging valuable for noninvasive assessment of renal damage in early-stage of chronic kidney disease?

PURPOSE

To evaluate the potential of 3 T magnetic resonance diffusion kurtosis imaging (DKI) in assessing the renal damage in early-stage of chronic kidney disease (CKD) patients with normal or slightly changed functional index, using histopathology as reference standard.

METHODS

49 CKD patients and 18 healthy volunteers were recruited in this study. CKD patients were divided into two groups based on estimated glomerular filtration rate (eGFR): Study group I (eGFR ≥ 90 ml/min/1.73 m2 [n = 20]) and Study group II (eGFR < 90 ml/min/1.73 m2 [n = 29]). DKI was performed in all participants. The DKI parameters (mean kurtosis [MK], mean diffusivity [MD], fractional anisotropy [FA]) of renal cortex and medulla were measured. The differences of parenchymal MD, MK and FA values among the different groups were compared. The correlations between DKI parameters and clinicopathological characteristics were assessed. Diagnostic performance of DKI to assess renal damage in early-stage of CKD was analyzed.

RESULTS

The cortex MD and MK showed significant difference among three groups (P < 0.05): trend of cortex MD: Study group II < Study group I < control group; trend of cortex MK: control group < Study group I < Study group II. The cortex MD and MK and medulla FA were correlated with eGFR and Interstitial fibrosis/Tubular atrophy score (0.3 < r < 0.5). Cortex MD and MK yielded an AUC of 0.752 for differentiating healthy volunteers from CKD patients with eGFR ≥ 90 ml/min/1.73 m2.

CONCLUSION

DKI shows potential in non-invasive and multi-parameter quantitative assessment of renal damage in early-stage of CKD patients and provide additional information for changes in renal function and histopathology.

Multiparametric Magnetic Resonance Investigations on Acute and Long-Term Kidney Injury

Acute kidney injury (AKI) is a frequent complication of critical illness and carries a significant risk of short- and long-term mortality. The prediction of the progression of AKI to long-term injury has been difficult for renal disease treatment. Radiologists are keen for the early detection of transition from AKI to long-term kidney injury, which would help in the preventive measures. The lack of established methods for early detection of long-term kidney injury underscores the pressing needs of advanced imaging technology that reveals microscopic tissue alterations during the progression of AKI. Fueled by recent advances in data acquisition and post-processing methods of magnetic resonance imaging (MRI), multiparametric MRI is showing great potential as a diagnostic tool for many kidney diseases. Multiparametric MRI studies offer a precious opportunity for real-time noninvasive monitoring of pathological development and progression of AKI to long-term injury. It provides insight into renal vasculature and function (arterial spin labeling, intravoxel incoherent motion), tissue oxygenation (blood oxygen level-dependent), tissue injury and fibrosis (diffusion tensor imaging, diffusion kurtosis imaging, T1 and T2 mapping, quantitative susceptibility mapping). The multiparametric MRI approach is highly promising but the longitudinal investigation on the transition of AKI to irreversible long-term impairment is largely ignored. Further optimization and implementation of renal MR methods in clinical practice will enhance our comprehension of not only AKI but chronic kidney diseases. Novel imaging biomarkers for microscopic renal tissue alterations could be discovered and benefit the preventative interventions. This review explores recent MRI applications on acute and long-term kidney injury while addressing lingering challenges, with emphasis on the potential value of the development of multiparametric MRI for renal imaging on clinical systems. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Evaluation of renal function in chronic kidney disease (CKD) by mDIXON-Quant and Amide Proton Transfer weighted (APTw) imaging

BACKGROUND

Chronic kidney disease (CKD) is a long-term condition that affects >10% of the adult population worldwide. Noninvasive assessment of renal function has important clinical significance for disease diagnosis and prognosis evaluation.

OBJECTIVE

To explore the value of mDIXON-Quant combined with amide proton transfer weighted (APTw) imaging for accessing renal function in chronic kidney disease (CKD).

MATERIALS AND METHODS

Twenty-two healthy volunteers (HVs) and 30 CKD patients were included in this study, and the CKD patients were divided into the mild CKD (mCKD) group (14 cases) and moderate-to-severe CKD (msCKD) group (16 cases) according to glomerular filtration rate (eGFR). The cortex APT (cAPT), medulla APT (mAPT), cortex R2⁎ (cR2⁎), medulla R2⁎ (mR2⁎), cortex FF (cFF) and medulla FF (mFF) values of the right renal were independently measured by two radiologists. Intra-group correlation coefficient (ICC) test was used to test the inter-observer consistency. The analysis of variance (ANOVA) was used to compare the difference among three groups. Mann-Whitney U test was used to analyze the differences of R2⁎, FF and APT values among the patient and HV groups. Area under the receiver operating characteristic (ROC) curve (AUC) was used to analyze the diagnostic efficiency. The corresponding threshold, sensitivity, and specificity were obtained according to the maximum approximate index. The combined diagnostic efficacy of R2⁎, FF, and APT values was analyzed by binary Logistic regression, and the AUC of combined diagnosis was compared with the AUC of the single parameter by the Delong test.

RESULTS

The cAPT value of the HV, mCKD and msCKD groups increased gradually. The mAPT value and cR2⁎ values of the mCKD and msCKD groups were higher than those of the HV group, while the mFF value of the mCKD group was lower than HV group (all P < 0.05). The cAPT and mAPT values showed good diagnostic efficacy in evaluating different degrees of renal damage, while cR2⁎ and mFF values showed moderate diagnostic efficacy. When combining the APT, R2⁎, and FF values, the diagnostic efficiency was significantly improved.

CONCLUSION

mDIXON-Quant combined APTw imaging can be used for improved diagnosis of CKD.