Diffusion-weighted, intravoxel incoherent motion, and diffusion kurtosis tensor MR imaging in chronic kidney diseases: Correlations with histology

Association between cardiometabolic index and kidney stone from NHANES: a population-based study

Purpose

The Cardiometabolic Index (CMI) is a novel marker of visceral obesity and dyslipidemia. Our study aimed to explore the association between CMI and kidney stones among US adults.

Methods

This cross-sectional study was conducted among adults with complete records of CMI and kidney stones information from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES). Inverse probability treatment weighting (IPTW) was used to balance the baseline characteristics of the study population. The independent relationship between CMI and kidney stones was evaluated using IPTW-adjusted multivariate logistic regression, restricted cubic splines (RCS), and subgroup analysis.

Results

A total of 9,177 participants, with an average CMI of 0.72 (0.99), were included in this study. The IPTW-adjusted logistic regression revealed that CMI was an independent risk factor for kidney stones. The adjusted odds ratio (OR) for kidney stones were 1.39 (95% CI: 1.24 - 1.56, P < 0.001) for the second CMI tertile and 1.31 (95% CI: 1.17 - 1.47, P < 0.001) for the third CMI tertile, compared with the first CMI tertile. A linear relationship between CMI levels and kidney stone risk was observed in the RCS analysis. Subgroup analysis showed that the association between CMI levels and kidney stone risk remained stable across groups.

Conclusions

A positive association between CMI level and the risk of kidney stones was observed among US adults in our study. Further large-scale prospective studies are needed to validate our findings.

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.

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.

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.