Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis in chronic kidney disease patients

Noninvasive diagnosis of interstitial fibrosis in chronic kidney disease: a systematic review and meta-analysis

RATIONALE AND OBJECTIVES

Researchers have delved into noninvasive diagnostic methods of renal fibrosis (RF) in chronic kidney disease, including ultrasound (US), magnetic resonance imaging (MRI), and radiomics. However, the value of these diagnostic methods in the noninvasive diagnosis of RF remains contentious. Consequently, the present study aimed to systematically delineate the accuracy of the noninvasive diagnosis of RF.

MATERIALS AND METHODS

A systematic search covering PubMed, Embase, Cochrane Library, and Web of Science databases for all data available up to 28 July 2023 was conducted for eligible studies.

RESULTS

We included 21 studies covering 4885 participants. Among them, nine studies utilized US as a noninvasive diagnostic method, eight studies used MRI, and four articles employed radiomics. The sensitivity and specificity of US for detecting RF were 0.81 (95% CI: 0.76-0.86) and 0.79 (95% CI: 0.72-0.84). The sensitivity and specificity of MRI were 0.77 (95% CI: 0.70-0.83) and 0.92 (95% CI: 0.85-0.96). The sensitivity and specificity of radiomics were 0.69 (95% CI: 0.59-0.77) and 0.78 (95% CI: 0.68-0.85).

CONCLUSIONS

The current early noninvasive diagnostic methods for RF include US, MRI, and radiomics. However, this study demonstrates that US has a higher sensitivity for the detection of RF compared to MRI. Compared to US, radiomics studies based on US did not show superior advantages. Therefore, challenges still exist in the current radiomics approaches for diagnosing RF, and further exploration of optimized artificial intelligence (AI) algorithms and technologies is needed.

Robust, fast and accurate mapping of diffusional mean kurtosis

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

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.

Assessing renal interstitial fibrosis using compartmental, non-compartmental, and model-free diffusion MRI approaches

OBJECTIVE

To assess renal interstitial fibrosis (IF) using diffusion MRI approaches, and explore whether corticomedullary difference (CMD) of diffusion parameters, combination among MRI parameters, or combination with estimated glomerular filtration rate (eGFR) benefit IF evaluation.

METHODS

Forty-two patients with chronic kidney disease were included, undergoing MRI examinations. MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medulla. CMD of these parameters was calculated. Pathological IF scores (1-3) were obtained by biopsy. Patients were divided into mild (IF = 1, n = 23) and moderate-severe fibrosis (IF = 2-3, n = 19) groups. Group comparisons for MRI parameters were performed. Diagnostic performances were assessed by the receiver operator's curve analysis for discriminating mild from moderate-severe IF patients.

RESULTS

Significant inter-group differences existed for cortical ADC, IVIM-D, IVIM-f, DKI-MD, DR-CSI VB, and DR-CSI VC. Significant inter-group differences existed in ΔADC, ΔMD, ΔVB, ΔVC, ΔQB, and ΔQC. Among the cortical MRI parameters, VB displayed the highest AUC = 0.849, while ADC, f, and MD also showed AUC > 0.8. After combining cortical value and CMD, the diagnostic performances of the MRI parameters were slightly improved except for IVIM-D. Combining VB with f brings the best performance (AUC = 0.903) among MRI bi-variant models. A combination of cortical VB, ΔADC, and eGFR brought obvious improvement in diagnostic performance (AUC 0.963 vs 0.879, specificity 0.826 vs 0.896, and sensitivity 1.000 vs 0.842) than eGFR alone.

CONCLUSION

Our study shows promising results for the assessment of renal IF using diffusion MRI approaches.

CRITICAL RELEVANCE STATEMENT

Our study explores the non-invasive assessment of renal IF, an independent and effective predictor of renal outcomes, by comparing and combining diffusion MRI approaches including compartmental, non-compartmental, and model-free approaches.

KEY POINTS

Significant difference exists for diffusion parameters between mild and moderate-severe IF. Generally, cortical parameters show better performance than corresponding CMD. Bi-variant model lifts the diagnostic performance for assessing IF.

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.

Noninvasive Assessment of the Renal Function, Oxford Classification and Prognostic Risk Stratification of IgAN by Using Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Blood Oxygenation Level-Dependent MRI

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. Oxford classification including mesangial hypercellularity (M), endothelial hypercellularity (E), segmental sclerosis (S), interstitial fibrosis/tubular atrophy (T), and crescent (C) were recommended to predict the prognosis of IgAN.

PURPOSE

To explore whether multiparametric magnetic resonance imaging (MRI) can be applied to assess the renal function, Oxford classification, and risk of progression to end-stage kidney disease within 5 years of IgAN.

STUDY TYPE

Prospective.

POPULATION

A total of 46 patients with pathologically confirmed IgAN and 20 healthy volunteers.

FIELD STRENGTH/SEQUENCE

A 3-T, blood oxygenation level-dependent (BOLD)-MRI, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

ASSESSMENT

Two radiologists measured the cortex and medulla T2*, apparent diffusion coefficient (ADC), true diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (fp). All participants were divided into three groups: group 1, healthy volunteers; group 2, patients with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 ; group 3, patients with eGFR <60 mL/min/1.73 m2 . Or two groups: group A, 5-year risk scores ≤10% and group B, 5-year risk scores >10%.

STATISTICAL TESTS

Intraclass correlation coefficient, one-way analysis of variance, least-significant difference, Student's t-test, Pearson product-moment correlation, Spearman's rank correlation, and receiver operating characteristics (ROC) with the area under the curve (AUC). A P value <0.05 was considered statistically significant.

RESULTS

Except for cortical Dp, all other MRI parameters showed significant differences between group 1 and group 2. None of the MRI parameters showed a significant correlation with M, E, or C scores. Cortical T2*, Dt, fp, and medullary Dt and fp showed low-to-moderate significant correlations with S scores. Except for cortical and medullary Dp, all other MRI parameters were significantly correlated with T scores. Cortical Dt showed the largest AUC for differentiating group A from group B (AUC = 0.927) and T0 from T1/T2 (AUC = 0.963).

DATA CONCLUSION

Imaging by IVIM-DWI and BOLD-MRI could facilitate noninvasive assessment of the renal function, Oxford classification, and prognostic risk of IgAN patients.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 3.

Monoexponential, biexponential, stretched-exponential and kurtosis models of diffusion-weighted imaging in kidney assessment: comparison between patients with primary aldosteronism and healthy controls

PURPOSE

This study used various diffusion-weighted imaging (DWI) models (including monoexponential, biexponential, stretched-exponential and kurtosis models) in renal magnetic resonance imaging (MRI) to compare whether there were differences in each diffusion parameter between patients with primary aldosteronism (PA) and healthy volunteers.

MATERIALS AND METHODS

Twenty-two (female:male, 14:8; age, 48 ± 10 years) patients with PA and 22 age- and sex-matched healthy controls (HCs) underwent MRI examinations of the kidneys. The independent-sample t test or the Mann‒Whitney U test was used to detect differences in the diffusion metrics of the kidneys between the two groups. Univariable and multivariable linear regression were applied to analyze the correlations between diffusion parameters and the clinical indicators.

RESULTS

The mean diffusivity (MD, p < 0.001) and radial diffusivity (Dr, p < 0.001) values in the medulla were lower in the PA group than in the HC group. The medullary fractional anisotropy (FA, p < 0.001) was higher than that of HCs. The FA (p < 0.001) and axial diffusivity (Da, p < 0.001) values in the cortex were lower in the PA group. The cortical α (anomalous exponent term, p = 0.016) was higher in the PA patients than in the HCs. Linear regression analysis showed that log(plasma aldosterone concentration) and the estimated glomerular filtration rate (eGFR) were correlated with medullary FA.

CONCLUSION

The stretched-exponential model (cortical α) and the kurtosis model (FA, MD and Dr in the medulla and FA and Da in the cortex) showed significant differences between PA patients and healthy volunteers and may have potential for noninvasive renal assessment in PA patients.

RtNet: a deep hybrid neural network for the identification of acute rejection and chronic allograft nephropathy after renal transplantation using multiparametric MRI

BACKGROUND

Reliable diagnosis of the cause of renal allograft dysfunction is of clinical importance. The aim of this study is to develop a hybrid deep-learning approach for determining acute rejection (AR), chronic allograft nephropathy (CAN) and renal function in kidney-allografted patients by multimodality integration.

METHODS

Clinical and magnetic resonance imaging (MRI) data of 252 kidney-allografted patients who underwent post-transplantation MRI between December 2014 and November 2019 were retrospectively collected. An end-to-end convolutional neural network, namely RtNet, was designed to discriminate between AR, CAN and stable renal allograft recipient (SR), and secondarily, to predict the impaired renal graft function [estimated glomerular filtration rate (eGFR) ≤50 mL/min/1.73 m2]. Specially, clinical variables and MRI radiomics features were integrated into the RtNet, resulting in a hybrid network (RtNet+). The performance of the conventional radiomics model RtRad, RtNet and RtNet+ was compared to test the effect of multimodality interaction.

RESULTS

Out of 252 patients, AR, CAN and SR was diagnosed in 20/252 (7.9%), 92/252 (36.5%) and 140/252 (55.6%) patients, respectively. Of all MRI sequences, T2-weighted imaging and diffusion-weighted imaging with stretched exponential analysis showed better performance than other sequences. On pairwise comparison of resulting prediction models, RtNet+ produced significantly higher macro-area-under-curve (macro-AUC) (0.733 versus 0.745; P = 0.047) than RtNet in discriminating between AR, CAN and SR. RtNet+ performed similarly to the RtNet (macro-AUC, 0.762 versus 0.756; P > 0.05) in discriminating between eGFR ≤50 mL/min/1.73 m2 and >50 mL/min/1.73 m2. With decision curve analysis, adding RtRad and RtNet to clinical variables resulted in more net benefits in diagnostic performance.

CONCLUSIONS

Our study revealed that the proposed RtNet+ model owned a stable performance in revealing the cause of renal allograft dysfunction, and thus might offer important references for individualized diagnostics and treatment strategy.

Renal functional and interstitial fibrotic assessment with non-Gaussian diffusion kurtosis imaging

OBJECTIVES

To evaluate the application value of diffusion kurtosis imaging (DKI) for monitoring renal function and interstitial fibrosis.

METHODS

Forty-two patients suspected of having primary nephropathy, hypertension or diabetes with impaired renal function were examined with DKI. DKI metrics of renal cortex and medulla on both sides of each patient were measured, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), mean diffusivity (MD) and fractional anisotropy (FA). The differences in DKI metrics between stable and impaired estimated glomerular filtration rate (eGFR) patients as well as between mild and severe interstitial fibrosis patients were compared. Correlations of DKI metrics with clinical indicators and pathology were analyzed. Diagnostic performance of DKI to assess the degree of renal dysfunction was analyzed.

RESULTS

Cortical MK, parenchymal Ka, MD and medullary FA were different in stable vs impaired eGFR patients and mild vs severe interstitial fibrosis patients (all p < .05). Negative correlation was found between Ka and eGFR (cortex: r = - 0.579; medulla: r = - 0.603), between MD and interstitial fibrosis (cortex: r = - 0.899; medulla: r = - 0.770), and positive correlation was found between MD and eGFR (cortex: r = 0.411; medulla: r = 0.344), between Ka and interstitial fibrosis (cortex: r = 0.871; medulla: r = 0.844) (all p < .05). DKI combined with mean arterial blood pressure (MAP) and urea showed good diagnostic power for assessing the degree of renal dysfunction (sensitivity: 90.5%; specificity: 89.5%).

CONCLUSIONS

Noninvasive DKI has certain application value for monitoring renal function and interstitial fibrosis.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.