Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging

Early and late assessment of renal allograft dysfunction using intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI): a prospective study

PURPOSE

To evaluate the ability of the Intravoxel Incoherent Motion (IVIM) and monoexponentially ADC in renal allograft function in the early and late phases of transplantation, and to predict their effectiveness in discrimination of the graft pathology.

METHODS

This is a prospective study included participants scanned with quantitative diffusion and perfusion sequences on a 3-T MR scanner (Philips, Ingenia); the ADC and IVIM parameters; were calculated. Correlations and regression analysis with the eGFR, transplantation periods, and pathology were assessed.

RESULTS

This study included 105 renal allograft recipients (85 males, and 20 females with mean age = 32.4 ± 11.9 years and age range = 22-61 years). There was a significant positive correlation between the whole parameters of the ADC and IVIM with eGFR however, the cortical parameters showed higher significant correlation coefficients (p < 0.001). Regression analysis revealed the most significant model can predict eGFR groups included cortical pseudo diffusion (D*) and cortical ADC (p < 0.001). In graft dysfunction eGFR was 61.5 ml/min and normal graft was 64 ml/min. This model demonstrates a high performance of an AUC 96% [0.93-0.97]. In the late transplantation, there is a higher correlation with D* compared to ADC, p-values = 0.001.

CONCLUSION

IVIM and ADC Values are significant biomarkers for renal allograft function assessment, cortical ADC, and D* had the highest performance even in situations with mild impairment that is not affect the eGFR yet as cases of proteinuria with normal eGFR. Furthermore, D* is superior to ADC in the late assessment of the renal transplant.

Effects of canagliflozin on kidney oxygenation evaluated using blood oxygenation level-dependent MRI in patients with type 2 diabetes

Background

Recent clinical studies suggest protective effects of SGLT2 inhibitors on kidney disease outcome. Chronic hypoxia has a critical role in kidney disease development, thus we speculated that canagliflozin, an SGLT2 inhibitor, can improve kidney oxygenation.

Methods

A single-arm study was conducted to investigate the effects of canagliflozin on T2* value, which reflects oxygenation level, in patients with type 2 diabetes (T2D) using repeated blood oxygenation level-dependent MRI (BOLD MRI) examinations. Changes in cortical T2* from before (Day 0) to after single-dose treatment (Day 1) and after five consecutive treatments (Day 5) were evaluated using 12-layer concentric objects (TLCO) and region of interest (ROI) methods.

Results

In the full analysis set (n=14 patients), the TLCO method showed no change of T2* with canagliflozin treatment, whereas the ROI method found that cortical T2* was significantly increased on Day 1 but not on Day 5. Sensitivity analysis using TLCO in 13 well-measured patients showed that canagliflozin significantly increased T2* on Day 1 with no change on Day 5, whereas a significant improvement in cortical T2* following canagliflozin treatment was found on both Day 1 and 5 using ROI.

Conclusions

Short-term canagliflozin treatment may improve cortical oxygenation and lead to better kidney outcomes in patients with T2D.

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.

Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) imaging. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m2, CKD stage G4-5); 172 with moderate renal dysfunction (30 ≤ eGFR < 60 mL/min/1.73 m2, CKD stage G3a/b); and 76 with mild renal dysfunction (eGFR ≥ 60 mL/min/1.73 m2, CKD stage G1-2) participated in this study. The model was applied to the right, left, and both kidneys, as well as to each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for the bilateral kidney models than for the unilateral kidney models. Our deep learning approach using kidney MRI can be applied to classify patients with CKD based on the severity of kidney disease.

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.

Non-invasive prediction of the chronic degree of lupus nephropathy based on ultrasound radiomics

OBJECTIVE

Through machine learning (ML) analysis of the radiomics features of ultrasound extracted from patients with lupus nephritis (LN), this attempt was made to non-invasively predict the chronicity index (CI)of LN.

METHODS

A retrospective collection of 136 patients with LN who had renal biopsy was retrospectively collected, and the patients were randomly divided into training set and validation set according to 7:3. Radiomics features are extracted from ultrasound images, independent factors are obtained by using LASSO dimensionality reduction, and then seven ML models were used to establish predictive models. At the same time, a clinical model and an US model were established. The diagnostic efficacy of the model is evaluated by analysis of the receiver operating characteristics (ROC) curve, accuracy, specificity, and sensitivity. The performance of the seven machine learning models was compared with each other and with clinical and US models.

RESULTS

A total of 1314 radiomics features are extracted from ultrasound images, and 5 features are finally screened out by LASSO for model construction, and the average ROC of the seven ML is 0.683, among which the Xgboost model performed the best, and the AUC in the test set is 0.826 (95% CI: 0.681-0.936). For the same test set, the AUC of clinical model constructed based on eGFR is 0.560 (95% CI: 0.357-0.761), and the AUC of US model constructed based on Ultrasound parameters is 0.679 (95% CI: 0.489-0.853). The Xgboost model is significantly more efficient than the clinical and US models.

CONCLUSION

ML model based on ultrasound radiomics features can accurately predict the chronic degree of LN, which can provide a valuable reference for clinicians in the treatment strategy of LN patients.

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.

Correlation of Renal Oxygenation with Renal Function in Chronic Kidney Disease: A Preliminary Prospective Study

INTRODUCTION

Chronic hypoxia is prevalent in chronic kidney disease (CKD), and blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI) provides noninvasive evaluation of renal oxygenation. This study aimed to explore the correlation of renal oxygenation evaluated by BOLD-MRI with renal function.

METHODS

97 non-dialysis patients with CKD stages 1-5 and healthy volunteers (HVs) were recruited in the study, all participants without diabetes. Based on their estimated glomerular filtration rate (eGFR), the patients were divided into two groups: CKD stages 1-3 (CKD 1-3) and CKD stages 4-5 (CKD 4-5). We measured cortical and medullary T2* (COT2* and MET2*) values in all participants by BOLD-MRI. Physiological indices were also recorded and compared among three groups. Correlation of T2* values with clinical characteristics was determined.

RESULTS

The COT2* values were significantly higher than MET2* values in all participants. The COT2* and MET2* values of three groups were ranked as HV > CKD 1-3> CKD 4-5 (p < 0.0001). There were positive correlations between the COT2* values, MET2* values and eGFR, hemoglobin (r > 0.4, p < 0.01). The 24-h urinary protein (24-h Upr) showed weak correlation with the COT2* value (rs = -0.2301, p = 0.0265) and no correlation with the MET2* value (p > 0.05). Urinary microprotein, including urinary alpha1-microglobulin, urinary beta2-microglobulin (β2-MG), and urinary retinol-binding protein (RBP), showed strong correlation with COT2* and MET2* values. According to the analysis of receiver operating characteristic curve, the optimal cut-points between HV and CKD 1-3 were "<61.17 ms" (sensitivity: 91.23%, specificity: 100%) for COT2* values and "<35.00 ms" (sensitivity: 77.19%, specificity: 100%) for MET2* values, whereas COT2* values ("<47.34 ms"; sensitivity: 90.00%, specificity: 92.98%) and MET2* values ("<25.09 ms"; sensitivity: 97.50%, specificity: 80.70%) between CKD 1-3 and CKD 4-5.

CONCLUSION

The decline of renal oxygenation reflected on T2* values, especially in cortex, may be an effective diagnostic marker for early detection of CKD.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

BACKGROUND

To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.

METHODS

A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.

RESULTS

The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively.

CONCLUSION

We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study

Introduction

Diabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.

Methods

For this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort (n1 = 49) and the testing cohort (n2 = 21). According to the estimated glomerular filtration rate (eGFR), patients were assigned into the normal renal function (normal-RF) group, the non-severe renal function impairment (non-sRI) group, and the severe renal function impairment (sRI) group. Based on the largest coronal image of T2WI, the speeded up robust features (SURF) algorithm was used for texture feature extraction. Analysis of variance (ANOVA) and relief and recursive feature elimination (RFE) were applied to select the important features and then support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms were used for the model construction. The values of area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis were used to assess their performance. The robust T2WI model was selected to construct a multimodal MRI model by combining the measured BOLD (blood oxygenation level-dependent) and diffusion-weighted imaging (DWI) values.

Results

The mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort.

Discussion

The model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function.

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

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.

Evaluating renal iron overload in diabetes mellitus by blood oxygen level-dependent magnetic resonance imaging: a longitudinal experimental study

BACKGROUND

Iron overload plays a critical role in the pathogenesis of diabetic nephropathy. Non-invasive evaluation of renal iron overload in diabetes in the management and intervention of diabetic nephropathy is of great significance. This study aimed to explore the feasibility of blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) in evaluating renal iron overload in diabetes using a rabbit model.

METHODS

The rabbits were randomly divided into control, iron-overload (I), diabetes (D), and diabetes with iron-overload (DI) groups (each n = 19). The diabetes models were generated by injecting intravenous alloxan solution, and the iron-overload models were generated by injecting intramuscular iron-dextran. BOLD MRI was performed immediately (week 0) and at week 4, 8, and 12 following modeling. The differences in renal cortex (CR2*) and outer medulla R2* (MR2*) and the ratio of MR2*-CR2* (MCR) across the different time points were compared.

RESULTS

Iron was first deposited in glomeruli in the I group and in proximal tubular cells in renal cortex in the D group. In the DI group, there was iron deposition in both glomeruli and proximal tubular cells at week 4, and the accumulation increased subsequently. The degree of kidney injury and iron overload was more severe in the DI group than those in the I and D groups at week 12. At week 8 and 12, the CR2* and MR2* in the DI group were higher than those in the I and D groups (all P < 0.05). The MCR in the I, D, and DI groups decreased from week 0 to 4 (all P < 0.001), and that in the I group increased from week 8 to 12 (P = 0.034). CR2* and MR2* values displayed different trends from week 0-12. Dynamic MCR curves in the D and DI groups were different from that in the I group.

CONCLUSION

It presents interactions between diabetes and iron overload in kidney injury, and BOLD MRI can be used to evaluate renal iron overload in diabetes.

The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model

We evaluated a multiclass classification model to predict estimated glomerular filtration rate (eGFR) groups in chronic kidney disease (CKD) patients using magnetic resonance imaging (MRI) texture analysis (TA). We identified 166 CKD patients who underwent MRI comprising Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images, apparent diffusion coefficient (ADC) maps, and T2* maps. The patients were divided into severe, moderate, and control groups based on eGFR borderlines of 30 and 60 mL/min/1.73 m2. After extracting 93 texture features (TFs), dimension reduction was performed using inter-observer reproducibility analysis and sequential feature selection (SFS) algorithm. Models were created using linear discriminant analysis (LDA); support vector machine (SVM) with linear, rbf, and sigmoid kernels; decision tree (DT); and random forest (RF) classifiers, with synthetic minority oversampling technique (SMOTE). Models underwent 100-time repeat nested cross-validation. Overall performances of our classification models were modest, and TA based on T1-weighted IP/OP/WO images provided better performance than those based on ADC and T2* maps. The most favorable result was observed in the T1-weighted WO image using RF classifier and the combination model was derived from all T1-weighted images using SVM classifier with rbf kernel. Among the selected TFs, total energy and energy had weak correlations with eGFR.

Machine Learning-Based Ultrasound Radiomics for Evaluating the Function of Transplanted Kidneys

The aim of the study described here was to investigate the value of different machine learning models based on the clinical and radiomic features of 2-D ultrasound images to evaluate post-transplant renal function (pTRF). We included 233 patients who underwent ultrasound examination after renal transplantation and divided them into the normal pTRF group (group 1) and the abnormal pTRF group (group 2) based on their estimated glomerular filtration rates. The patients with abnormal pTRF were further subdivided into the non-severe renal function impairment group (group 2A) and the severe impairment group (group 2B). The radiomic features were extracted from the 2-D ultrasound images of each case. The clinical and ultrasound image features as well as radiomic features from the training set were selected, and then five machine learning algorithms were used to construct models for evaluating pTRF. Receiver operating characteristic curves were used to evaluate the discriminatory ability of each model. A total of 19 radiomic features and one clinical feature (age) were retained for discriminating group 1 from group 2. The area under the receiver operating characteristic curve (AUC) values of the models ranged from 0.788 to 0.839 in the test set, and no significant differences were found between the models (all p values >0.05). A total of 17 radiomic features and 1 ultrasound image feature (thickness) were retained for discriminating group 2A from group 2B. The AUC values of the models ranged from 0.689 to 0.772, and no significant differences were found between the models (all p values >0.05). Machine learning models based on clinical and ultrasound image features, as well as radiomics features, from 2-D ultrasound images can be used to evaluate pTRF.

Fluctuation of R2* values in blood oxygenation level-dependent MRI during acute and remission phases of IgA vasculitis with nephritis in children

PURPOSE

Noninvasive assessment of the kidney using blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) has progressed remarkably; indications have expanded to include the evaluation of glomerulonephritis. However, no longitudinal measurements from acute to post-treatment remission phases have been reported. Hence, this study aimed to investigate spin relaxation rate (R2*) values during acute and remission phases in children with glomerulonephritis.

MATERIALS AND METHODS

All pediatric patients with IgA vasculitis with nephritis (IgAVN) diagnosed between January 2014 and October 2021 and requiring renal biopsy were retrospectively reviewed; four patients who were observed from onset to remission were included in this study. In total, eight MRIs were performed in the acute and remission phases, and R2* values and fluctuations induced by low-dose oxygen administration were determined from 10 echoes using a 1.5 T MRI system with 4.76-47.6 ms echo times and a 153 ms repetition time.

RESULTS

The median age of patients undergoing MRI was 8.5 years in the acute phase and 13.9 years in the remission phase. R2* values of the acute phase were higher than those of the remission phase; however, the difference was not significant (cortex; p = 0.32 and medulla; p = 0.052). Oxygen administration did not cause fluctuations in the R2* values in the cortex or medulla during the acute phase (cortex; p = 0.67 and medulla; p = 0.76); however, in the remission phase, the R2* values in the cortex and medulla significantly decreased due to low-dose oxygen administration (cortex; p < 0.01 and medulla; p < 0.01).

CONCLUSION

The fluctuation in R2* values observed during different phases of IgAVN indicates that BOLD MRI may be used to assess disease activity. Therefore, we propose BOLD MRI with low-dose oxygen administration as a noninvasive method to evaluate the activity of glomerulonephritis.

Assessment of renal function using magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses

Background

The incidence of chronic kidney disease (CKD) is high, and is easy to develop into end-stage renal disease (ESRD), which requires kidney dialysis or kidney transplantation. Therefore, we want to explore the clinical value of magnetic resonance quantitative histogram analysis based on spatial labeling with multiple inversion pulses (SLEEK) in assessing renal function in the early stage.

Methods

One hundred and twenty-nine patients underwent abdominal MRI examination, including a coronal SLEEK sequence. The patients were divided into the control group [CG, 47 cases, estimated glomerular filtration rate (eGFR) >90], the mild renal function impairment (mRI) group (48 cases, eGFR =60–90), and the moderate to severe renal function impairment (m-sRI) group (34 cases, eGFR <60). Two experienced radiologists delineated cortex and medulla regions of interest (ROIs) on SLEEK images to obtain cortex and medulla quantitative histogram parameters [Mean, Median, Percentiles (5^th, 10^th, 25^th, 75^th, and 90^th), Skewness, Kurtosis, and Entropy] using FireVoxel. These histogram parameters were compared by proper statistical methods such as one-way analysis of variance, the χ² test, and receiver operating characteristic (ROC) curve analysis.

Results

Four histogram parameters (Inhomogeneity_cortex, Skewness_cortex, Kurtosis_medulla, and Entropy_medulla) differed significantly between the CG and the mRI group. One medulla (Entropy_medulla) and nine cortex (Mean_cortex, Median_cortex, Kurtosis_cortex, Entropy_cortex, and 5^th, 10^th, 25^th, 75^th, and 90^th Percentile_cortex) histogram parameters were significantly different between the m-RI and m-sRI groups. The most relevant parameter to eGFR was Inhomogenity_cortex (r=−0.450, P<0.001). Inhomogeneity_cortex had the largest area under the curve (AUC) for differentiating the mRI group from the CG (AUC =0.718; 95% CI: 0.616–0.806), while 25^th Percentile_cortex generated the largest AUC (AUC =0.786; 95% CI: 0.681–0.869) for differentiating the mRI and m-sRI groups.

Conclusions

Quantitative histogram parameters based on a SLEEK sequence can be used to supplement renal dysfunction assessment. Cortex histogram parameters are more valuable for evaluating renal function than medulla histogram parameters.

Restricted diffusion MRI as a functional biomarker for the assessment of acute calcular upper urinary tract obstruction: initial experience

Background

Acute renal obstruction due to stone is a prevalent scenario. The diffusion-weighted magnetic resonance imaging (DWI) of the kidneys provides a noninvasive information on renal function. Our objective is to prospectively assess the potential role of DWI to predict the signal changes of a kidney with acute calcular obstruction.

Results

Chi-square and Fissure exact tests were used to assess the association of diffusion signal changes among patients and control groups. Cohen's Kappa test was run to determine the degree of agreement between the two radiologists. An independent sample t-test was performed to assess the significant difference among ADC values between the two groups. Restricted signals of the obstructed kidneys showed a statistically significant difference when compared with contralateral unobstructed kidney and control group with p value (0.001) and (0.01), respectively. Furthermore, there is a moderate agreement between the two radiologists K = 0.7, p = 0. 001. There is no statistically significant difference in ADC values when comparing the obstructed kidney and the contralateral unobstructed kidney of the patient group or with the control group p value (0.06) and (0.05), respectively.

Conclusion

Restricted signals of the obstructed kidney by DWI may be a helpful tool in diagnosing acute unilateral renal obstruction and can affect its management; however, it needs further validation by more studies.

Basic principles and new advances in kidney imaging

Over the past few years, clinical renal imaging has seen great advances, allowing assessments of kidney structure and morphology, perfusion, function and metabolism, and oxygenation, as well as microstructure and the interstitium. Medical imaging is becoming increasingly important in the evaluation of kidney physiology and pathophysiology, showing promise in management of patients with renal disease, in particular with regard to diagnosis, classification, and prediction of disease development and progression, monitoring response to therapy, detection of drug toxicity, and patient selection for clinical trials. A variety of imaging modalities, ranging from routine to advanced tools, are currently available to probe the kidney both spatially and temporally, particularly ultrasonography, computed tomography, positron emission tomography, renal scintigraphy, and multiparametric magnetic resonance imaging. Given that the range is broad and varied, kidney imaging techniques should be chosen based on the clinical question and the specific underlying pathologic mechanism, taking into account contraindications and possible adverse effects. Integration of various modalities providing complementary information will likely provide the greatest insight into renal pathophysiology. This review aims to highlight major recent advances in key tools that are currently available or potentially relevant for clinical kidney imaging, with a focus on non-oncological applications. The review also outlines the context of use, limitations, and advantages of various techniques, and highlights gaps to be filled with future development and clinical adoption.

Texture analysis based on quantitative magnetic resonance imaging to assess kidney function: a preliminary study

Background

Magnetic resonance imaging (MRI) has demonstrated its potential in the evaluation of renal function. Texture analysis (TA) is a novel technique to quantify tissue heterogeneity. We aim to investigate the feasibility of using TA based on the apparent diffusion coefficient (ADC), as well as T1 and T2 maps to evaluate renal function.

Methods

Patients with impaired renal function and subjects with a normal renal function who underwent renal diffusion weighted imaging (DWI), as well as T1 and T2 mapping at 3T, were prospectively enrolled. The participants were classified into four groups according to the estimated glomerular filtration rate (eGFR, mL/min/1.73 m²): normal (eGFR ≥90), mildly impaired (60≤ eGFR <90), moderately impaired (30≤ eGFR <60), and severely impaired (eGFR <30) renal function groups. Texture features quantified from the renal cortex or medulla were selected to build classifiers to discriminate different renal function groups by plotting receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

In total, 116 candidates were included (94 patients and 22 healthy volunteers, mean age 37.9±14.9 years). There were 46 participants in the normal renal function group, 14 in the mildly impaired renal function group, 27 in the moderately impaired renal function group, and 29 in the severely impaired renal function group. Texture features from the ADC and T1 maps exhibited a good correlation to eGFR. The AUC, sensitivity, specificity, PPV, and NPV to differentiate between the normal and impaired renal function groups were 0.835, 0.792, 0.867, 0.905, and 0.722, respectively; to differentiate between the mildly impaired and moderately impaired groups were 0.937, 0.889, 0.857, 0.923, and 0.800, respectively; and to differentiate between the moderately impaired and severely impaired groups was 0.940, 0.759, 0.889, 0.880, and 0.774, respectively.

Conclusions

TA based on ADC and T1 maps is feasible for evaluating renal function with relatively good accuracy.

Susceptibility weighted imaging (SWI) for evaluating renal dysfunction in type 2 diabetes mellitus: a preliminary study using SWI parameters and SWI-based texture features

Background

Susceptibility weighted imaging (SWI) could reflect tissue blood oxygen levels, and then whether it could be used to evaluate renal injury remains to be further studied. This study aimed to examine the performance of SWI parameters and SWI-based texture features in evaluating renal dysfunction of type 2 diabetes mellitus (T2DM).

Methods

Forty-five patients with T2DM were included. With the estimated glomerular filtration rate (eGFR), the patients were divided into non-moderate-severe renal injured group (non-msRI, eGFR >60 mL/min/1.73 m²) and moderate-severe renal injured group (msRI, eGFR ≤60 mL/min/1.73 m²). The 3 SWI parameters and 16 SWI-based texture features between non-msRI and msRI were compared. The correlation between the parameters and BUN, Scr was analyzed.

Results

The signal intensity ratio of the medulla to psoas muscle (MPswi) was significantly lower than the signal intensity ratio of the cortex to psoas muscle (CPswi) in non-msRI and msRI group (t=8.619, 3.483, respectively, P<0.05). MPswi was higher, and the signal intensity ratio of the cortex to the medulla (CMswi), Skewness, Correlation were lower in msRI than in non-msRI (P<0.05). These parameters showed similar diagnostic efficacies for msRI (P>0.05), and AUCs were 0.703–0.854. CMswi was an independent protective factor for msRI (OR =0.026, P=0.003). MPswi and CMswi were correlated with BUN (r=0.416, −0.545, P<0.05). CMswi and Correlation were correlated with Scr (r=−0.645, −0.411, P<0.05).

Conclusions

SWI was valuable for assessing renal dysfunction, which may be helpful for the evaluation of moderate-severe renal injured patients with T2DM.

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.

New insights into diabetes mellitus and its complications: a narrative review

Diabetes is a metabolic disorder accompanied by complications of multiple organs and systems. Diabetic nephropathy (DN) is one of the most prevalent lethal complications of diabetes. Although numerous biomarkers have be clarified for early diagnosis of DN, renal biopsy is still the gold standard. As a noninvasive imaging diagnostic method, blood oxygen level-dependent (BOLD) MRI can help understand the kidney oxygenation status and fibrosis process and monitor the efficacy of new drugs for DN via monitoring renal blood oxygen levels. Recent studies have shown that noncoding RNAs including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) were all involved in the development of DN, which could be exploited as therapeutic strategy to control DN. Dyslipidemia is also a common complication of diabetes. Apolipoprotein M (apoM), as a novel apolipoprotein, may be related to the development and progression of diabetes, which need to further investigation. Obstructive sleep apnea (OSA) is another common complication of diabetes and is an independent risk factor for cardiovascular disease (CVD). At present, there is no simple, effective and rapid diagnostic method to early identification of OSA in patients with diabetes. A nomogram consisted of waist-to-hip ratio, smoking status, body mass index, serum uric acid, HOMA-IR and history of fatty liver might be an alternative method to early assess the risk of OSA.

DTI-based radiomics signature for the detection of early diabetic kidney damage

OBJECTIVE

To explore whether a radiomics signature based on diffusion tensor imaging (DTI) can detect early kidney damage in diabetic patients.

MATERIALS AND METHODS

Twenty-eight healthy volunteers (group A) and thirty type 2 diabetic patients (group B) with micro-normoalbuminuria, a urinary albumin-to-creatinine ratio (ACR) < 30 mg/g and an estimated glomerular filtration rate (eGFR) of 60-120 mL/(min 1.73 m²) were recruited. Kidney DTI was performed using 1.5T magnetic resonance imaging (MRI).The radiologist manually drew regions of interest (ROI) on the fractional anisotropy (FA) map of the right kidney ROI including the cortex and medulla. The texture features of the ROIs were extracted using MaZda software. The Fisher coefficient, mutual information (MI), and probability of classification error and average correlation coefficient (POE + ACC) methods were used to select the texture features. The most valuable texture features were further selected by the least absolute shrinkage and selection operator (LASSO) algorithm. A LASSO regression model based on the radiomics signature was established. The diagnostic performance of the model for detecting early diabetic kidney changes was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Empower (R), R, and MedCalc15.8 software were used for statistical analysis RESULTS: A total of 279 texture features were extracted from ROI of the kidney, and 30 most valuable texture features were selected from groups A and B using MaZda software. After LASSO-logistic regression, a diagnostic model of diabetic kidney damage based on texture features was established. Model discrimination evaluation: AUC = 0.882 (0.770 ± 0.952). Model calibration evaluation: Hosmer-Lemeshow X2 = 5.3611, P = 0.7184, P > 0.05, the model has good calibration.

CONCLUSION

The texture features based on DTI could play a promising role in detecting early diabetic kidney damage.

Evaluation of renal ischemia-reperfusion injury by magnetic resonance imaging texture analysis: An experimental study

PURPOSE

To explore the value of MRI texture analysis in evaluating the presence and severity of early renal ischemia-reperfusion injury (IRI).

METHODS

Healthy New Zealand rabbits were used (IRI group, N = 54; control group, N = 8). Rabbits in the IRI group underwent left renal artery clamping for 60 minutes. Magnetic resonance imaging was performed before and at 1, 12, 24, and 48 hours after IRI. The relationship between MRI texture features and histopathology parameters was assessed using Pearson's correlation coefficients. The diagnostic performance of texture features in kidney differentiation at different time points was assessed by receiver operating characteristic curve analysis.

RESULTS

T₂ WI_S(3,-3)Inverse_Difference_Moment had the strongest correlation with brush border destruction, tubular epithelial edema, necrosis, and cast (r = 0.56, -0.58, 0.62, and 0.69, respectively; all P < .001). BOLD_S(4,-4)Correlation had the strongest correlation with interstitial inflammatory cell infiltration (r = 0.63, P < .001). SWI_S(4,4)Difference_Entropy had the strongest correlation with microvessel density (r = 0.61, P < .001). The areas under the curve for T₂ WI_S(3,-3)Inverse_Difference_Moment, SWI_S(4,4)Difference_Entropy, and BOLD_S(4,-4)Correlation in kidney differentiation before IRI and that at 1 and 12 hours after reperfusion were 0.76, 0.72, and 0.70, respectively; the values before IRI and at 24 and 48 hours after reperfusion were 0.84, 0.81, and 0.69, respectively. The area under the curve for T₂ WI_S(3,-3)Inverse_Difference_Moment in kidney differentiation at 1 and 12 hours after reperfusion and that at 24 and 48 hours after reperfusion was 0.66.

CONCLUSION

Magnetic resonance imaging texture analysis can be used for evaluating the presence and severity of early renal IRI.