Elastography ultrasound with machine learning improves the diagnostic performance of traditional ultrasound in predicting kidney fibrosis

Shear-wave elastography in renal stiffness in children with hematuria and/or proteinuria

BACKGROUND

We sought to evaluate renal stiffness in children with hematuria and/or proteinuria using shear wave elastography (SWE) and to investigate the clinical value of renal stiffness in children with hematuria and/or proteinuria.

METHODS

According to the results of urinary occult blood and urinary protein tests, 349 pediatric patients were categorized into one of four groups: pure hematuria (HU), pure proteinuria (PU), concomitant hematuria and proteinuria (HUPU), or control (non-HUPU). Patient demographic data, laboratory test results, and renal ultrasound data were collected.

RESULTS

There were significant differences in cortical/medullary elasticity among the four groups (the most sensitive cutoff value between HU and PU was 1.72) (P < 0.05). We found that hematuria and proteinuria interacted with renal cortical elasticity (P < 0.05) but that hematuria and proteinuria did not interact with renal medullary elasticity or cortical/medullary elasticity (P > 0.05). Renal elasticity values correlated with sex, age, body surface area, body mass index, qualitative urinary protein, urine N-acetyl-β-D-glucosaminidase, 24-hour urinary protein quantity, renal volume, and renal cortical thickness (P < 0.05).

CONCLUSIONS

SWE can be used to detect changes in renal stiffness in children with hematuria and/or proteinuria. SWE is beneficial for the early detection of glomerular disease in children with abnormal urine test results.

IMPACT

This study evaluated the utility of shear wave elastography for the assessment of renal elasticity in pediatric patients presenting with hematuria and/or proteinuria. Children with pure proteinuria had significantly higher renal cortical/medullary elasticity values than those with pure hematuria. An interaction effect between hematuria and proteinuria on renal cortical stiffness was observed. Shear wave elastography can be used as a tool to assess early renal injury in children with urinalysis abnormalities.

End-to-End CT Radiomics-Based Pipeline for Predicting Renal Interstitial Fibrosis Grade in CKD Patients

RATIONALE AND OBJECTIVES

Non-invasive assessment of renal fibrosis in patients with chronic kidney disease (CKD) remains a clinical challenge. This study aims to integrate radiomics and clinical factors to develop an end-to-end pipeline for predicting interstitial fibrosis (IF) in CKD patients.

MATERIALS AND METHODS

This retrospective study included 80 patients with CKD, with 53 patients in training set and 27 patients in test set. All patients underwent renal computed tomography (CT) scans and biopsy. Patients were classified into two groups based on their renal IF grade: mild-moderate and severe. Radiomics features were extracted from the automatically segmented right renal region on CT images, and univariate analysis along with multiple Least Absolute Shrinkage and Selection Operator (LASSO) was employed to construct the radiomics signature. Subsequently, logistic regression models were developed to create the radiomics model and the combined model. The predictive performance of both models was evaluated through discrimination, calibration, and decision curve analysis (DCA), and a nomogram was constructed for the model demonstrating superior performance.

RESULTS

The combined model significantly outperformed the radiomics model, achieving a cross-validated AUC of 0.935±0.041 in the training set, compared to 0.804±0.024 for the radiomics model. In the test set, the combined model outperformed the radiomics model, with an AUC of 0.918 [95% CI 0.799-1] vs. 0.764 [95% CI 0.549-0.979], p=0.031 (DeLong test, Statistic: -2.152). Calibration curves and DCA indicated that the combined model demonstrated good calibration and better clinical net benefit.

CONCLUSION

This end-to-end workflow could serve as a potential non-invasive tool to predict renal IF grade (mild-moderate vs. severe) in CKD 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.

Interpretable machine learning model integrating clinical and elastosonographic features to detect renal fibrosis in Asian patients with chronic kidney disease

BACKGROUND

Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastosonographic features to distinguish moderate-severe fibrosis from mild fibrosis among CKD patients.

METHODS

A total of 162 patients with CKD who underwent shear wave elastography examinations and renal biopsies at our institution were prospectively enrolled. Four classifiers using machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbor (KNN), which integrated elastosonographic features and clinical characteristics, were established to differentiate moderate-severe renal fibrosis from mild forms. The area under the receiver operating characteristic curve (AUC) and average precision were employed to compare the performance of constructed models, and the SHapley Additive exPlanations (SHAP) strategy was used to visualize and interpret the model output.

RESULTS

The XGBoost model outperformed the other developed machine learning models, demonstrating optimal diagnostic performance in both the primary (AUC = 0.97, 95% confidence level (CI) 0.94-0.99; average precision = 0.97, 95% CI 0.97-0.98) and five-fold cross-validation (AUC = 0.85, 95% CI 0.73-0.98; average precision = 0.90, 95% CI 0.86-0.93) datasets. The SHAP approach provided visual interpretation for XGBoost, highlighting the features' impact on the diagnostic process, wherein the estimated glomerular filtration rate provided the largest contribution to the model output, followed by the elastic modulus, then renal length, renal resistive index, and hypertension.

CONCLUSION

This study proposed an XGBoost model for distinguishing moderate-severe renal fibrosis from mild forms in CKD patients, which could be used to assist clinicians in decision-making and follow-up strategies. Moreover, the SHAP algorithm makes it feasible to visualize and interpret the feature processing and diagnostic processes of the model output.

Shear wave elastography parameters adds prognostic value to adverse outcome in kidney transplantation recipients

INTRODUCTION

The tissue stiffness of donor kidneys in transplantation may increase due to pathological changes such as glomerulosclerosis and interstitial fibrosis, and those changes associate worse outcomes in kidney transplantation recipients. Ultrasound elastography is a noninvasive imaging examination with the ability to quantitatively reflect tissue stiffness. Aim of this study was to evaluate the prognostic value of ultrasound elastography for adverse kidney outcome in kidney transplantation recipients.

METHODS

Shear wave elastography (SWE) examinations were performed by two independent operators in kidney transplantation recipients. The primary outcome was a composite of kidney graft deterioration, all-cause re-hospitalization, and all-cause mortality. Survival analysis was calculated by Kaplan-Meier curves with the log-rank test and Cox regression analysis.

RESULTS

A total of 161 patients (mean age 46 years, 63.4% men) were followed for a median of 20.1 months. 27 patients (16.77%) reached the primary endpoint. The mean and median tissue stiffness at the medulla (hazard ratio: 1.265 and 1.229, respectively), estimated glomerular filtration rate (eGFR), and serum albumin level were associated with the primary outcome in univariate Cox regression. Adding mean or median medulla SWE to a baseline model containing eGFR and albumin significantly improved its discrimination (C-statistics: 0.736 for the baseline, 0.766 and 0.772 for the model added mean and median medulla SWE, respectively).

CONCLUSION

The medullary tissue stiffness of kidney allograft measured by shear wave elastography may provide incremental prognostic value to adverse outcomes in kidney transplantation recipients. Including SWE parameters in kidney transplantation recipients management could be considered to improve risk stratification.

Random forest method for estimation of brake specific fuel consumption

The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC). BSFC map plays a crucial role in the analysis, optimization, and assessment of internal combustion engines. However, due to cost constraints, some values on the BSFC map are estimated using techniques like K-nearest neighbor, inverse distance weighted interpolation, and multi-layer perceptron, which are recognized for their limited accuracy, particularly when dealing with distributed sampled data. To address this, an improved random forest method is proposed for the estimation of BSFC. Polynomial features are employed to increase higher dimensions of features for random forest by nonlinear transformation, and critical parameters are optimized by particle swarm optimization algorithms. The performance of different methods was compared on two datasets to estimate 20%, 30%, and 40% of BSFC data, and the results reveal that the method proposed in this paper outperforms other common methods and is suitable for estimating the BSFC map.

Kidney fibrosis: Emerging diagnostic and therapeutic strategies

An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.

Ultrasound elastography in chronic kidney disease: a systematic review and meta-analysis

Ultrasound elastography (USE) is a noninvasive technique for assessing tissue elasticity, and its application in nephrology has aroused growing interest in recent years. The purpose of this article is to systematically review the clinical application of USE in patients with chronic kidney disease (CKD), including native and transplanted kidneys, and quantitatively investigate differences in elasticity values between healthy individuals and CKD patients. Furthermore, we provide a qualitative analysis of the studies included, discussing the potential interplay between renal stiffness, estimated glomerular filtration rate, and fibrosis. In January 2022, a systematic search was carried out on the MEDLINE (PubMed) database, concerning studies on the application of USE in patients with CKD, including patients with transplanted kidneys. The results of the included studies were extracted by two independent researchers and presented mainly through a formal narrative summary. A meta-analysis of nine study parts from six studies was performed. A total of 647 studies were screened for eligibility and, after applying the exclusion and inclusion criteria, 69 studies were included, for a total of 6728 patients. The studies proved very heterogeneous in terms of design and results. The shear wave velocity difference of - 0.82 m/s (95% CI: - 1.72-0.07) between CKD patients and controls was not significant. This result agrees with the qualitative evaluation of included studies that found controversial results for the relationship between renal stiffness and glomerular filtration rate. On the contrary, a clear relationship seems to emerge between USE values and the degree of fibrosis. At present, due to the heterogeneity of results and technical challenges, large-scale application in the monitoring of CKD patients remains controversial.

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.

Shear Wave Elastography for Assessment of Biopsy-Proven Renal Fibrosis: A Systematic Review and Meta-analysis

The purpose of this meta-analysis was to evaluate the diagnostic performance of shear wave elastography (SWE) for the staging of renal fibrosis in patients with chronic kidney disease (CKD). Classification of CKD into mild, moderate and severe fibrosis was based on renal biopsy pathology (glomerulosclerosis, tubulointerstitial injury and vascular sclerosis). The Cochrane Library, Medline, PubMed, Web of Science, EMBASE and CNKI databases were searched from January 1, 2009, to April 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio and area under the receiver operating characteristic curve (AUROC) were calculated using random effects models. A total of 1394 patients from 14 studies were included in the final analysis. For mild, moderate and severe renal fibrosis, SWE had a sensitivity of 0.79 (95% confidence interval [CI]: 0.67-0.88), 0.73 (95% CI: 0.65-0.80) and 0.87 (95% CI: 0.71-0.95); a specificity of 0.82 (95% CI: 0.75-0.87), 72% (95% CI: 0.67-0.77) and 0.83 (95% CI: 0.80-0.86); an AUROC of 0.87 (95% CI: 0.84-0.90), 0.78 (95% CI: 0.75-0.82) and 0.86 (95% CI: 0.82-0.88); and a diagnostic odds ratio of 17 (95% CI: 7-43), 7 (95% CI: 4-12) and 34 (95% CI: 13-88), respectively. Meta-regressions revealed that the publication date, system used and number of valid measurements of SWE were the main causes of heterogeneity. SWE is a good technique for diagnosing mild and severe renal fibrosis, as well as a fair technique for diagnosing moderate fibrosis.

Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease

OBJECTIVES

To predict kidney fibrosis in patients with chronic kidney disease using radiomics of two-dimensional ultrasound (B-mode) and Sound Touch Elastography (STE) images in combination with clinical features.

METHODS

The Mindray Resona 7 ultrasonic diagnostic apparatus with SC5-1U convex array probe (bandwidth frequency of 1-5 MHz) was used to perform two-dimensional ultrasound and STE software. The severity of cortical tubulointerstitial fibrosis was divided into three grades: mild interstitial fibrosis and tubular atrophy (IFTA), fibrotic area < 25%; moderate IFTA, fibrotic area 26-50%; and severe IFTA, fibrotic area > 50%. After extracting radiomics from B-mode and STE images in these patients, we analyzed two classification schemes: mild versus moderate-to-severe IFTA, and mild-to-moderate versus severe IFTA. A nomogram was constructed based on multiple logistic regression analyses, combining clinical and radiomics. The performance of the nomogram for differentiation was evaluated using receiver operating characteristic (ROC), calibration, and decision curves.

RESULTS

A total of 150 patients undergoing kidney biopsy were enrolled (mild IFTA: n = 74; moderate IFTA: n = 33; severe IFTA: n = 43) and randomized into training (n = 105) and validation cohorts (n = 45). To differentiate between mild and moderate-to-severe IFTA, a nomogram incorporating STE radiomics, albumin, and estimated glomerular filtration (eGFR) rate achieved an area under the ROC curve (AUC) of 0.91 (95% confidence interval [CI]: 0.85-0.97) and 0.85 (95% CI: 0.77-0.98) in the training and validation cohorts, respectively. Between mild-to-moderate and severe IFTA, the nomogram incorporating B-mode and STE radiomics features, age, and eGFR achieved an AUC of 0.93 (95% CI: 0.89-0.98) and 0.83 (95% CI: 0.70-0.95) in the training and validation cohorts, respectively. Finally, we performed a decision curve analysis and found that the nomogram using both radiomics and clinical features exhibited better predictability than any other model (DeLong test, p < 0.05 for the training and validation cohorts).

CONCLUSION

A nomogram based on two-dimensional ultrasound and STE radiomics and clinical features served as a non-invasive tool capable of differentiating kidney fibrosis of different severities.

KEY POINTS

• Radiomics calculated based on the ultrasound imaging may be used to predict the severities of kidney fibrosis. • Radiomics may be used to identify clinical features associated with the progression of tubulointerstitial fibrosis in patients with CKD. • Non-invasive ultrasound imaging-based radiomics method with accuracy aids in detecting renal fibrosis with different IFTA severities.

ALTERATION IN SHEAR WAVE ELASTOGRAPHY IS ASSOCIATED WITH ACUTE KIDNEY INJURY: A PROSPECTIVE OBSERVATIONAL PILOT STUDY

ABSTRACT

Background: Kidney stiffness could change during kidney disease. We hypothesize that acute kidney injury (AKI) would increase renal stiffness. Therefore, evaluating kidney Young's modulus (YM; a measure of tissue stiffness) using shear wave elastography (SWE) might help to diagnose AKI. Methods: This research was divided into two studies. Study A: Male C57BL/6 mice were used to observe kidney YM changes induced by sepsis-associated AKI, which was established by cecal ligation and puncture (CLP). Study B included 54 consecutive critically ill patients with or without AKI. Changes in renal YM were observed. Results: Study A: CLP mice showed a significantly higher kidney YM compared with the sham group. The YM gradually increased from CLP 0 hours to CLP 24 hours, and presented a fair relationship with the renal tubular injury score ( R2 = 0.71) and serum creatinine ( R2 = 0.73). Study B: YM was easily accessible, and the intraclass correlation coefficient ranged from 0.62 to 0.84. Kidney YM was higher in AKI patients and gradually increased from non-AKI to AKI III patients. Furthermore, the YM in the upper, middle, and lower poles of the renal cortex presented a fair relationship with kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin ( R2 ranging from 0.4 to 0.58), and the areas under the curve of the above five indicators for the diagnosis of AKI were 0.7, 0.73, 0.70, 0.74, and 0.79, respectively. Conclusion: SWE-derived estimates of renal stiffness are higher in AKI patients and sepsis-associated AKI mice. However, it has no advantage over NGAL and KIM-1. Trial Registration: Chinese Clinical Trial Registry No: ChiCTR2200061725. Retrospectively registered July 1, 2022, https://www.chictr.org.cn/showproj.aspx?proj=169359 .

Magnetic Resonance Elastography as Surrogate Marker of Interstitial Fibrosis in Kidney Transplantation: A Prospective Study

Background

Fibrosis progression is a major prognosis factor in kidney transplantation. Its assessment requires an allograft biopsy, which remains an invasive procedure at risk of complications.

Methods

We assessed renal stiffness by magnetic resonance elastography (MRE) as a surrogate marker of fibrosis in a prospective cohort of kidney transplant recipients compared with the histologic gold standard. Interstitial fibrosis was evaluated by three methods: the semi-quantitative Banff ci score, a visual quantitative evaluation by a pathologist, and a computer-assisted quantitative evaluation. MRE-derived stiffness was assessed at the superior, median, and inferior poles of the allograft.

Results

We initially enrolled 73 patients, but only 55 had measurements of their allograft stiffness by MRE before an allograft biopsy. There was no significant correlation between MRE-derived stiffness at the biopsy site and the ci score (ρ=-0.25, P=0.06) or with the two quantitative assessments (pathologist: ρ=-0.25, P=0.07; computer assisted: ρ=-0.21, P=0.12). We observed negative correlations between the stiffness of both the biopsy site and the whole allograft, with either the glomerulosclerosis percentage (ρ=-0.32, P=0.02 and ρ=-0.31, P=0.02, respectively) and the overall nephron fibrosis percentage, defined as the mean of the percentages of glomerulosclerosis and interstitial fibrosis (ρ=-0.30, P=0.02 and ρ=-0.28, P=0.04, respectively). At patient level, mean MRE-derived stiffness was similar across the three poles of the allograft (±0.25 kPa). However, a high variability of mean stiffness was found between patients, suggesting a strong influence of confounding factors. Finally, no significant correlation was found between mean MRE-derived stiffness and the slope of eGFR (P=0.08).

Conclusions

MRE-derived stiffness does not directly reflect the extent of fibrosis in kidney transplantation.