Functional MRI as a Tool for Evaluating Interstitial Fibrosis and Prognosis in Kidney Disease

AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (VRP), renal cortex volume (VRC), renal medulla volume (VRM), the CT values of renal parenchyma (HuRP), the CT values of renal cortex (HuRC), and the CT values of renal medulla (HuRM) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = - 0.759, - 0.777, - 0.420, - 0.762, - 0.771, and - 0.726, respectively, all p < 0.001). For predicting CKD in males, VRP had an area under the curve (AUC) of 0.726, p < 0.001; VRC, AUC 0.765, p < 0.001; VRM, AUC 0.578, p = 0.018; HuRP, AUC 0.912, p < 0.001; HuRC, AUC 0.952, p < 0.001; and HuRM, AUC 0.772, p < 0.001 in males. In females, VRP had an AUC of 0.813, p < 0.001; VRC, AUC 0.851, p < 0.001; VRM, AUC 0.623, p = 0.060; HuRP, AUC 0.904, p < 0.001; HuRC, AUC 0.934, p < 0.001; and HuRM, AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in HuRP are 99.9 Hu for males and 98.4 Hu for females, while in HuRC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between VRC, HuRP, and HuRC with renal function, while the association between VRP and HuRM was weaker, and the association between VRM was the weakest. Particularly, HuRP and HuRC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: HuRP < 99.9 Hu and HuRC < 120.1 Hu in males, and HuRP < 98.4 Hu and HuRC < 111.8 Hu in females.

Associations of Kidney Functional Magnetic Resonance Imaging Biomarkers with Markers of Inflammation in Individuals with CKD

Key Points

Lower baseline apparent diffusion coefficient, indicative of greater cortical fibrosis, correlated with higher baseline concentrations of serum markers of inflammation. No association between baseline cortical R2* and baseline serum markers of inflammation were found. Baseline kidney functional magnetic resonance imaging biomarkers of fibrosis and oxygenation were not associated with changes in inflammatory markers over time, which may be due to small changes in kidney function in the study.

Background

Greater fibrosis and decreased oxygenation may amplify systemic inflammation, but data on the associations of kidney functional magnetic resonance imaging (fMRI) measurements of fibrosis (apparent diffusion coefficient [ADC]) and oxygenation (relaxation rate [R2*]) with systemic markers of inflammation are limited.

Methods

We evaluated associations of baseline kidney fMRI-derived ADC and R2* with baseline and follow-up serum IL-6 and C-reactive protein (CRP) in 127 participants from the CKD Optimal Management with Binders and NicotinamidE trial, a randomized, 12-month trial of nicotinamide and lanthanum carbonate versus placebo in individuals with CKD stages 3–4. Cross-sectional analyses of baseline kidney fMRI biomarkers and markers of inflammation used multivariable linear regression. Longitudinal analyses of baseline kidney fMRI biomarkers and change in markers of inflammation over time used linear mixed-effects models.

Results

Mean±SD eGFR, ADC, and R2* were 32.2±8.7 ml/min per 1.73 m2, 1.46±0.17×10−3 mm2/s, and 20.3±3.1 s−1, respectively. Median (interquartile range) IL-6 and CRP were 3.7 (2.4–4.9) pg/ml and 2.8 (1.2–6.3) mg/L, respectively. After multivariable adjustment, IL-6 and CRP were 13.1% and 27.3% higher per 1 SD decrease in baseline cortical ADC, respectively. Baseline cortical R2* did not have a significant association with IL-6 or CRP. Mean annual IL-6 and CRP slopes were 0.98 pg/ml per year and 0.91 mg/L per year, respectively. Baseline cortical ADC and R2* did not have significant associations with change in IL-6 or CRP over time.

Conclusions

Lower cortical ADC, suggestive of greater fibrosis, was associated with higher systemic inflammation. Baseline kidney fMRI biomarkers did not associate with changes in systemic markers of inflammation over time.

Native T1-mapping as a predictor of progressive renal function decline in chronic kidney disease patients

BACKGROUND

To investigate the potential of Native T1-mapping in predicting the prognosis of patients with chronic kidney disease (CKD).

METHODS

We enrolled 119 CKD patients as the study subjects and included 20 healthy volunteers as the control group, with follow-up extending until October 2022. Out of these patients, 63 underwent kidney biopsy measurements, and these patients were categorized into high (25-50%), low (< 25%), and no renal interstitial fibrosis (IF) (0%) groups. The study's endpoint event was the initiation of renal replacement therapy, kidney transplantation, or an increase of over 30% in serum creatinine levels. Cox regression analysis determined factors influencing unfavorable kidney outcomes. We employed Kaplan-Meier analysis to contrast kidney survival rates between the high and low T1 groups. Additionally, receiver-operating characteristic (ROC) curve analysis assessed the predictive accuracy of Native T1-mapping for kidney endpoint events.

RESULTS

T1 values across varying fibrosis degree groups showed statistical significance (F = 4.772, P < 0.05). Multivariate Cox regression pinpointed 24-h urine protein, cystatin C(CysC), hemoglobin(Hb), and T1 as factors tied to the emergence of kidney endpoint events. Kaplan-Meier survival analysis revealed a markedly higher likelihood of kidney endpoint events in the high T1 group compared to the low T1 value group (P < 0.001). The ROC curves for variables (CysC, T1, Hb) tied to kidney endpoint events demonstrated area under the curves(AUCs) of 0.83 (95%CI: 0.75-0.91) for CysC, 0.77 (95%CI: 0.68-0.86) for T1, and 0.73 (95%CI: 0.63-0.83) for Hb. Combining these variables elevated the AUC to 0.88 (95%CI: 0.81-0.94).

CONCLUSION

Native T1-mapping holds promise in facilitating more precise and earlier detection of CKD patients most at risk for end-stage renal disease.

Fibrosis in Chronic Kidney Disease: Pathophysiology and Therapeutic Targets

Chronic kidney disease (CKD) is a slowly progressive condition characterized by decreased kidney function, tubular injury, oxidative stress, and inflammation. CKD is a leading global health burden that is asymptomatic in early stages but can ultimately cause kidney failure. Its etiology is complex and involves dysregulated signaling pathways that lead to fibrosis. Transforming growth factor (TGF)-β is a central mediator in promoting transdifferentiation of polarized renal tubular epithelial cells into mesenchymal cells, resulting in irreversible kidney injury. While current therapies are limited, the search for more effective diagnostic and treatment modalities is intensive. Although biopsy with histology is the most accurate method of diagnosis and staging, imaging techniques such as diffusion-weighted magnetic resonance imaging and shear wave elastography ultrasound are less invasive ways to stage fibrosis. Current therapies such as renin-angiotensin blockers, mineralocorticoid receptor antagonists, and sodium/glucose cotransporter 2 inhibitors aim to delay progression. Newer antifibrotic agents that suppress the downstream inflammatory mediators involved in the fibrotic process are in clinical trials, and potential therapeutic targets that interfere with TGF-β signaling are being explored. Small interfering RNAs and stem cell-based therapeutics are also being evaluated. Further research and clinical studies are necessary in order to avoid dialysis and kidney transplantation.

Cardiovascular magnetic resonance imaging in myocardial involvement of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder that primarily affects young women. Myocardial involvement in SLE frequently occurs and it is rather challenging to make the diagnosis in current clinical settings, mainly due to the extensive clinical presentation of signs and symptoms. As a noninvasive imaging reference in diagnosing cardiomyopathy and myocarditis, cardiovascular magnetic resonance (CMR) imaging can provide new insight into myocardial abnormalities including inflammation, fibrosis, and microcirculation. Therefore, the main aim of this work was to systematically review the pathology, clinical features, and diagnosis, while illustrating the clinical role of CMR on myocardial involvement of SLE.

Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review

Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.

Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches

BACKGROUND

 Until today, assessment of renal function has remained a challenge for modern medicine. In many cases, kidney diseases accompanied by a decrease in renal function remain undetected and unsolved, since neither laboratory tests nor imaging diagnostics provide adequate information on kidney status. In recent years, developments in the field of functional magnetic resonance imaging with application to abdominal organs have opened new possibilities combining anatomic imaging with multiparametric functional information. The multiparametric approach enables the measurement of perfusion, diffusion, oxygenation, and tissue characterization in one examination, thus providing more comprehensive insight into pathophysiological processes of diseases as well as effects of therapeutic interventions. However, application of multiparametric fMRI in the kidneys is still restricted mainly to research areas and transfer to the clinical routine is still outstanding. One of the major challenges is the lack of a standardized protocol for acquisition and postprocessing including efficient strategies for data analysis. This article provides an overview of the most common fMRI techniques with application to the kidney together with new approaches regarding data analysis with deep learning.

METHODS

 This article implies a selective literature review using the literature database PubMed in May 2021 supplemented by our own experiences in this field.

RESULTS AND CONCLUSION

 Functional multiparametric MRI is a promising technique for assessing renal function in a more comprehensive approach by combining multiple parameters such as perfusion, diffusion, and BOLD imaging. New approaches with the application of deep learning techniques could substantially contribute to overcoming the challenge of handling the quantity of data and developing more efficient data postprocessing and analysis protocols. Thus, it can be hoped that multiparametric fMRI protocols can be sufficiently optimized to be used for routine renal examination and to assist clinicians in the diagnostics, monitoring, and treatment of kidney diseases in the future.

KEY POINTS

  · Multiparametric fMRI is a technique performed without the use of radiation, contrast media, and invasive methods.. · Multiparametric fMRI provides more comprehensive insight into pathophysiological processes of kidney diseases by combining functional and structural parameters.. · For broader acceptance of fMRI biomarkers, there is a need for standardization of acquisition, postprocessing, and analysis protocols as well as more prospective studies.. · Deep learning techniques could significantly contribute to an optimization of data acquisition and the postprocessing and interpretation of larger quantities of data..

CITATION FORMAT

· Zhang C, Schwartz M, Küstner T et al. Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8633.

Diffusion kurtosis imaging and arterial spin labeling for the noninvasive evaluation of persistent post-contrast acute kidney injury

OBJECTIVE

We investigated whether diffusion kurtosis imaging (DKI) and arterial spin labeling (ASL) facilitated the assessment of serial alterations in persistent post-contrast acute kidney injury (PC-AKI).

MATERIALS AND METHODS

We randomly divided 24 rats into four PC-AKI groups (days 1, 3, 7, and 13, n = 6/group), with an additional six control animals. We conducted functional magnetic resonance imaging (MRI), diffusion kurtosis imaging (DKI), and arterial spin-labeling (ASL) analyses. Mean kurtosis (MK), axial kurtosis (Ka), mean diffusivity (MD), fractional anisotropy (FA), radial kurtosis (Kr), and renal blood flow (RBF) maps were normalized to baseline (prior to contrast injection) to calculate adjusted △RBF, △MK, △Ka, △MD, △FA, and △Kr values. We also investigated urinary neutrophil gelatinase associated lipocalin (NGAL), serum cystatin C (CysC), aquaporin-2 (AQP2), hypoxia-inducible factor-1 (HIF-1α), and histological indices.

RESULTS

In the inner stripe of the outer medulla, when compared with controls, decreased △FA and △MD levels were observed on days 1, 3, and 7, and a distinct elevation in △MK and △Kr on days 1-13, and a persistent decrease in △RBF on days 1-13, and a prominent increase in △Ka on days 7 and 13 in PC-AKI animals (all p < 0.05). △Ka and △MK were positively correlated with AQP-2 (r = 0.8086, p < 0.0001 and r = 0.7314, p < 0.0001, respectively), and △RBF was highly correlated with HIF-1α (r = -0.7592, p < 0.0001). Moreover, both CysC and NGAL were significantly elevated in PC-AKI animals when compared with controls from days 1-13 (all p < 0.05). Renal histological data indicated severe tubular and glomerular injury at days 1-13 in all PC-AKI groups.

CONCLUSION

ASL and DKI may be noninvasively and longitudinally used to detect PC-AKI and predict further outcomes.

Evaluation of Renal Fibrosis by Mapping Histology and Magnetic Resonance Imaging

Background

Renal fibrosis is a key driver of progression in chronic kidney disease (CKD). Recent advances in diagnostic imaging techniques have shown promising results for the noninvasive assessment of renal fibrosis. However, the specificity and accuracy of these techniques are controversial because they indirectly assess renal fibrosis. This limits fibrosis assessment by imaging in CKD for clinical practice. To validate magnetic resonance imaging (MRI) assessment for fibrosis, we derived representative models by mapping histology-proven renal fibrosis and imaging in CKD.

Methods

Ninety-seven adult Chinese CKD participants with histology were studied. The kidney cortex interstitial extracellular matrix volume was calculated by the Aperio ScanScope system using Masson's trichrome slices. The kidney cortex microcirculation was quantitatively assessed by peritubular capillary density using CD34 staining. The imaging techniques included intravoxel incoherent motion diffusion-weighted imaging and magnetic resonance elastography (MRE) imaging. Relevant analyses were performed to evaluate the correlations between MRI parameters and histology variables. Multiple linear regression models were used to describe the relationships between a response variable and other variables. The best-fit lines, which minimize the sum of squared residuals of the multiple linear regression models, were generated.

Results

MRE values were negatively associated with the interstitial extracellular matrix volume (Rho = -0.397, p < 0.001). The best mapping model of extracellular matrix volume with the MRE value and estimated glomerular filtration rate (eGFR) we obtained was as follows: Interstitial extracellular matrix volume = 218.504 - 14.651 × In(MRE) - 18.499 × In(eGFR). DWI-fraction values were positively associated with peritubular capillary density (Rho = 0.472, p < 0.001). The best mapping model of peritubular capillary density with DWI-fraction value and eGFR was as follows: Peritubular capillaries density = 17.914 + 9.403 × (DWI - fraction) + 0.112 × (eGFR).

Conclusions

The study provides histological evidence to support that MRI can effectively evaluate fibrosis in the kidney. These findings picture the graphs of the mapping model from imaging and eGFR into fibrosis, which has significant value for clinical implementation.

Recent advances in medical image processing for the evaluation of chronic kidney disease

Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.