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

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.

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images

BACKGROUND

Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival.

METHODS

This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC.

RESULTS

Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77.

CONCLUSION

This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

Single photon emission computed tomography/computed tomography imaging of gouty arthritis: A new voice

Gouty arthritis, often referred to simply as gout, is a disorder of purine metabolism characterized by the deposition of monosodium urate monohydrate (MSU) crystals in multiple systems and organs, especially in joints and their surrounding soft tissue. Gout is a treatable chronic disease, and the main strategy for effective management is to reverse the deposition of MSU crystals by uric acid reduction, and to prevent gout attacks, tophi deposition and complications, and thereby improve the quality of life. However, the frequent association of gout with other conditions such as hypertension, obesity, cardiovascular disease, diabetes, dyslipidemia, chronic kidney disease (CKD) and kidney stones can complicate the treatment of gout and lead to premature death. Here, we review the use of medical imaging techniques for studying gouty arthritis with special interest in the potential role of single photon emission computed tomography (SPECT)/computed tomography (CT) in the clinical management of gout and complications (e.g., chronic kidney disease and cardiovascular disease).

Noninvasive Assessment of Diabetic Kidney Disease With MRI: Hype or Hope?

Owing to the increasing prevalence of diabetic mellitus, diabetic kidney disease (DKD) is presently the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early identification and disease interception is of paramount clinical importance for DKD management. However, current diagnostic, disease monitoring and prognostic tools are not satisfactory, due to their low sensitivity, low specificity, or invasiveness. Magnetic resonance imaging (MRI) is noninvasive and offers a host of contrast mechanisms that are sensitive to pathophysiological changes and risk factors associated with DKD. MRI tissue characterization involves structural and functional information including renal morphology (kidney volume (TKV) and parenchyma thickness using T1- or T2-weighted MRI), renal microstructure (diffusion weighted imaging, DWI), renal tissue oxygenation (blood oxygenation level dependent MRI, BOLD), renal hemodynamics (arterial spin labeling and phase contrast MRI), fibrosis (DWI) and abdominal or perirenal fat fraction (Dixon MRI). Recent (pre)clinical studies demonstrated the feasibility and potential value of DKD evaluation with MRI. Recognizing this opportunity, this review outlines key concepts and current trends in renal MRI technology for furthering our understanding of the mechanisms underlying DKD and for supplementing clinical decision-making in DKD. Progress in preclinical MRI of DKD is surveyed, and challenges for clinical translation of renal MRI are discussed. Future directions of DKD assessment and renal tissue characterization with (multi)parametric MRI are explored. Opportunities for discovery and clinical break-through are discussed including biological validation of the MRI findings, large-scale population studies, standardization of DKD protocols, the synergistic connection with data science to advance comprehensive texture analysis, and the development of smart and automatic data analysis and data visualization tools to further the concepts of virtual biopsy and personalized DKD precision medicine. We hope that this review will convey this vision and inspire the reader to become pioneers in noninvasive assessment and management of DKD with MRI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

DenseHillNet: a lightweight CNN for accurate classification of natural images

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the "glacier" and "mountain" categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.

Bone mineral density in haemophilia patients: A systematic review and meta-analysis

INTRODUCTION

With the increase in life expectancy of haemophilia patients (PWH), the risk of osteoporosis increases, but there is little research on whether haemophilia is the cause of osteoporosis.

AIM

To conduct systematically review whether bone mineral density (BMD) in PWH decreased and the factors affecting BMD.

METHODS

Two authors independently searched databases and reviewed citations from relevant articles, selecting studies published in any language and performed in humans before March 2023. Eligibility criteria were observational studies in PWH, with BMD as at least one outcome other than osteoporosis or bone loss, and analyses in a group of PWH and healthy controls.

RESULTS

Twelve studies were ultimately identified, consisting of 1210 individuals (534 PWH and 676 healthy controls), compared with the control group, BMD in PWH decreased by 0.13 g/cm2 [95% confidence interval (CI) -0.18 to -0.08, I2  = 89%]. No evidence of publication bias was detected. There was no evidence that age, BMI, level of physical activity, the types of haemophilia, haemophilia severity, a blood-borne virus (HCV) and treatment modality predicted the BMD in PWH.

CONCLUSION

The results indicate that BMD in PWH is lower than in healthy controls. Therefore, we strongly recommend PWH early measurement of BMD to prevent osteoporosis.

Imaging-based deep learning in kidney diseases: recent progress and future prospects

Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.

Diffusion Tensor and Kurtosis MRI-Based Radiomics Analysis of Kidney Injury in Type 2 Diabetes

BACKGROUND

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can provide quantitative parameters that show promise for evaluation of diabetic kidney disease (DKD). The combination of radiomics with DTI and DKI may hold potential clinical value in detecting DKD.

PURPOSE

To investigate radiomics models of DKI and DTI for predicting DKD in type 2 diabetes mellitus (T2DM) and evaluate their performance in automated renal parenchyma segmentation.

STUDY TYPE

Prospective.

POPULATION

One hundred and sixty-three T2DM patients (87 DKD; 63 females; 27-80 years), randomly divided into training cohort (N = 114) and validation cohort (N = 49).

FIELD STRENGTH/SEQUENCE

1.5-T, diffusion spectrum imaging (DSI) with 9 different b-values.

ASSESSMENT

The images of DSI were processed to generate DKI and DTI parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). The Swin UNETR model was trained with 5-fold cross-validation using 100 samples for renal parenchyma segmentation. Subsequently, radiomics features were automatically extracted from each parameter map. The performance of the radiomics models on the validation cohort was evaluated by utilizing the receiver operating characteristic (ROC) curve.

STATISTICAL TESTS

Mann-Whitney U test, Chi-squared test, Pearson correlation coefficient, least absolute shrinkage and selection operator (LASSO), dice similarity coefficient (DSC), decision curve analysis (DCA), area under the curve (AUC), and DeLong's test. The threshold for statistical significance was set at P < 0.05.

RESULTS

The DKI_MD achieved the best segmentation performance (DSC, 0.925 ± 0.011). A combined radiomics model (DTI_FA, DTI_MD, DKI_FA, DKI_MD, and DKI_RD) showed the best performance (AUC, 0.918; 95% confidence interval [CI]: 0.820-0.991). When the threshold probability was greater than 20%, the combined model provided the greatest net benefit. Among the single parameter maps, the DTI_FA exhibited superior diagnostic performance (AUC, 887; 95% CI: 0.779-0.972).

DATA CONCLUSION

The radiomics signature constructed based on DKI and DTI may be used as an accurate and non-invasive tool to identify T2DM and DKD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.7 mo, 18 patients were confirmed to have benefited from the treatment, defined as a 20% improvement from baseline in the estimated glomerular filtration rate. A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype characteristics. Radiomics features, comprising 116 handcrafted features and 78 deep learning features, were extracted from the affected renal and perirenal adipose regions. More features from the latter were correlated with early outcomes, as determined by univariate analysis, and were visually represented in radiomics heatmaps and volcano plots. After using consensus clustering and the least absolute shrinkage and selection operator method for feature selection, five machine learning models were evaluated. Logistic regression yielded the highest leave-one-out cross-validation accuracy of 0.780 (95%CI: 0.660-0.880) for the renal signature, while the support vector machine achieved 0.865 (95%CI: 0.769-0.942) for the perirenal adipose signature. SHapley Additive exPlanations was used to visually interpret the prediction mechanism, and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature, respectively. Multivariate analysis revealed that both signatures served as independent predictive factors. When combined, they achieved an area under the receiver operating characteristic curve of 0.888 (95%CI: 0.784-0.992), indicating that the imaging phenotypes from both regions complemented each other. In conclusion, non-contrast CT-based radiomics can be leveraged to predict the early outcomes of PTRA, thereby assisting in identifying patients with ARAS suitable for this treatment, with perirenal adipose tissue providing added predictive value.

A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI

Accurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis method for the automatic estimation of renal perfusion based on perfusion magnetic resonance imaging. Specifically, non-contrasted pseudo-continuous arterial spin labeling (PCASL) images are used for kidney transplant evaluation and perfusion estimation, as a biomarker of the status of the allograft. The proposed method uses machine/deep learning tools for the segmentation and classification of renal cortical and medullary tissues and automates the estimation of perfusion values. Data from 16 transplant patients has been used for the experiments. The automatic analysis of differentiated tissues within the kidney, such as cortex and medulla, is performed by employing the time-intensity-curves of non-contrasted T1-weighted MRI series. Specifically, using the Dice similarity coefficient as a figure of merit, results above 93%, 92% and 82% are obtained for whole kidney, cortex, and medulla, respectively. Besides, estimated cortical and medullary perfusion values are considered to be within the acceptable ranges within clinical practice.

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.

mDIXON-Quant for differentiation of renal damage degree in patients with chronic kidney disease

Background

Chronic kidney disease (CKD) is a complex syndrome with high morbidity and slow progression. Early stages of CKD are asymptomatic and lack of awareness at this stage allows CKD to progress through to advanced stages. Early detection of CKD is critical for the early intervention and prognosis improvement.

Purpose

To assess the capability of mDIXON-Quant imaging to detect early CKD and evaluate the degree of renal damage in patients with CKD.

Study type

Retrospective.

Population

35 patients with CKD: 18 cases were classifified as the mild renal damage group (group A) and 17 cases were classifified as the moderate to severe renal damage group (group B). 22 healthy volunteers (group C).

Field strength/sequence

A 3.0 T/T1WI, T2WI and mDIXON-Quant sequences.

Assessment

Transverse relaxation rate (R2*) values and fat fraction (FF) values derived from the mDIXON-Quant were calculated and compared among the three groups.

Statistical tests

The intra-class correlation (ICC) test; Chi-square test or Fisher's exact test; Shapiro-Wilk test; Kruskal Wallis test with adjustments for multiplicity (Bonferroni test); Area under the receiver operating characteristic (ROC) curve (AUC). The significance threshold was set at P < 0.05.

Results

Cortex FF values and cortex R2* values were significantly different among the three groups (P=0.028, <0.001), while medulla R2* values and medulla FF values were not (P=0.110, 0.139). Cortex FF values of group B was significantly higher than that of group A (Bonferroni adjusted P = 0.027). Cortex R2* values of group A and group B were both significantly higher than that of group C (Bonferroni adjusted P = 0.012, 0.001). The AUC of cortex FF values in distinguishing group A and group B was 0.766. The diagnostic efficiency of cortex R2* values in distinguishing group A vs. group C and group B vs. group C were 0.788 and 0.829.

Conclusion

The mDIXON-Quant imaging had a potential clinical value in early diagnosis of CKD and assessing the degree of renal damage in CKD patients.

Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has taken on a significant and increasing role in diagnostic procedures and treatments for patients who suffer from chronic kidney disease. Careful segmentation of kidneys from DCE-MRI scans is an essential early step towards the evaluation of kidney function. Recently, deep convolutional neural networks have increased in popularity in medical image segmentation. To this end, in this paper, we propose a new and fully automated two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in DCE-MRI scans. We first develop two convolutional neural networks that rely on the U-Net structure (UNT) to predict a kidney probability map for DCE-MRI scans. Then, to leverage the segmentation performance, the pixel-wise kidney probability map predicted from the deep model is exploited with the shape prior information in a level set method to guide the contour evolution towards the target kidney. Real DCE-MRI datasets of 45 subjects are used for training, validating, and testing the proposed approach. The valuation results demonstrate the high performance of the two-phase approach, achieving a Dice similarity coefficient of 0.95 ± 0.02 and intersection over union of 0.91 ± 0.03, and 1.54 ± 1.6 considering a 95% Hausdorff distance. Our intensive experiments confirm the potential and effectiveness of that approach over both UNT models and numerous recent level set-based methods.

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.

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.

A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).

Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features

Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.

Integration of artificial intelligence and multi-omics in kidney diseases

Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney's shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels' fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.

Theranostic roles of machine learning in clinical management of kidney stone disease

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.

Clinical study on the use of advanced magnetic resonance imaging in lupus nephritis

OBJECTIVES

To investigate the correlation between the histopathology of the kidney and clinical indicators in patients with lupus nephritis (LN) using magnetic resonance imaging (MRI).

METHODS

A total 50 female participants were enrolled in the study. Thirty patients with LN were divided into types 2, 3, 4, and 5, according to their pathological features. The control group consisted of 20 healthy female volunteers. Serum creatinine, C3, C1q, and anti-ds-DNA were measured. Conventional MRI, DTI, DWI, and BOLD scanning was performed to obtain the FA, ADC, and R2* values for the kidney.

RESULTS

Compared with the control group, FA and the ADC were decreased in patients with LN, while the R2* value was increased (P < 0.05). The overall comparison of the SLEDAI (Activity index of systemic lupus erythematosus) score, total pathological score, AI, and serum creatinine C3 showed that these were significantly different between the two groups (P < 0.05). FA and the ADC were negatively correlated with urinary, blood ds-DNA, and serum creatinine and positively correlated with C1q (P < 0.05). The R2* value was positively correlated with urinary NGAL, blood ds-DNA, and serum creatinine (P < 0.05). FA and the ADC were negatively correlated with the SLEDAI score, total pathological score, AI, CI, nephridial tissue C3, and C1q. The R2* value was positively correlated with the SLEDAI score, total pathological score, AI, CI, nephridial tissue C3, and C1q (P < 0.05).

CONCLUSIONS

MRI examination in female patients with LN was correlated with pathologic test results, which may have clinical significance in determining the disease's severity, treatment, and outcome.

Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures

Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m2. Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 ± 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796−0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460−0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling.

Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading

Purpose: To evaluate the usefulness of computed tomography (CT) texture descriptors integrated with machine-learning (ML) models in the identification of clear cell renal cell carcinoma (ccRCC) and for the first time papillary renal cell carcinoma (pRCC) tumor nuclear grades [World Health Organization (WHO)/International Society of Urologic Pathologists (ISUP) 1, 2, 3, and 4]. Approach: A total of 143 ccRCC and 21 pRCC patients were analyzed in this study. Texture features were extracted from late arterial phase CT images. A complete separation of training/validation and testing subsets from the beginning to the end of the pipeline was adopted. Feature dimension was reduced by collinearity analysis and Gini impurity-based feature selection. The synthetic minority over-sampling technique was employed for imbalanced datasets. The ML classifiers were logistic regression, SVM, RF, multi-layer perceptron, and K -NN. The differentiation between low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and between all grades was assessed for ccRCC and pRCC datasets. The classification performance was assessed and compared by certain metrics. Results: Textures-based classifiers were able to efficiently identify ccRCC and pRCC grades. An accuracy and area under the characteristic operating curve (AUC) up to 91%/0.9, 91%/0.9, 90%/0.9, and 88%/1 were reached when discriminating ccRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. An accuracy and AUC up to 96%/1, 81%/0.8, 86%/0.9, and 88%/0.9 were found when differentiating pRCC low grades/ high grades, grade 1/grade 2, grade 3/grade 4, and all grades, respectively. Conclusion: CT texture-based ML models can be used to assist radiologist in predicting the WHO/ISUP grade of ccRCC and pRCC pre-operatively.

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.

Comparison of Diagnostic Value for Chronic Kidney Disease between 640-Slice Computed Tomography Kidney Scan and Conventional Computed Tomography Scan

Objective

To explore the diagnostic value for chronic kidney disease (CKD) between 640-slice computed tomography (CT) kidney scan and conventional CT scan.

Methods

A total of 120 CKD patients who received kidney plain scan plus enhanced examination in the CT room of the Medical Imaging Department of our hospital from June 2019 to September 2019 were selected and randomly divided into the experimental group (n = 60) and the control group (n = 60). Patients in the control group received the conventional CT plain scan and enhanced scan, and for patients in the experimental group, CT plain scan was performed first, the range of 640-slice CT dynamic volume scan was determined, and after bolus injection of contrast agent, dynamic volume scan was performed for scanning in the cortical phase, myeloid phase, and secretory phase. The imaging quality and effective scanning dose were compared between the two modalities, and the relationship between CT values obtained from 640-slice CT scan and conventional CT scan and the renal impairment was analyzed.

Results

Compared with the control group, the image quality of 640-slice CT scan conducted in the experimental group was significantly better (P < 0.05); the effective radiation doses of the experimental group and the control group were, respectively, (1.89 ± 0.32) mSv and (3.26 ± 0.47) mSv, indicating that the dose was significantly lower in the experimental group than in the control group (t = 18.664, P < 0.001), and the correlation analysis showed that the relationship between the sum of CT values in the cortical phase of both kidneys and kidney injury in the experimental group was r = 0.835, P < 0.001.

Conclusion

Both 640-slice CT kidney scan and conventional CT scan can be used in the diagnosis of CKD. 640-slice CT has a lower radiation dose, better image quality, and higher application value.

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.

Machine Learning for Renal Pathologies: An Updated Survey

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Amide proton transfer magnetic resonance imaging to evaluate renal impairment in patients with chronic kidney disease

OBJECTIVE

We aimed to investigate the value of amide proton transfer magnetic resonance imaging (APT-MRI) in the classification of chronic kidney disease (CKD).

MATERIALS AND METHODS

A total of 30 patients with chronic kidney disease (CKD) and 25 healthy volunteers were enrolled in this study. Patients with chronic kidney disease were divided into two groups according to glomerular filtration rates: mild and moderate-to-severe renal impairment. Differences in cortical and medullary APT values were compared, and the correlation between corticomedullary APT values and glomerular filtration rates was analyzed. Data were statistically analyzed using SPSS 23.0.

RESULTS

Based on glomerular filtration rates, 14 patients were assigned to the mild renal impairment group, and 16 were assigned to the moderate-to-severe renal impairment group. Both of the cortical and medullary APT values showed a gradually increasing trend in the control, the mild, and the moderate-to-severe renal impairment groups. Cortical APT values were higher than medullary APT values in all the control and renal impairment groups (P < 0.05). APT values of the right renal cortex (r = -0.80, P < 0.05) and medulla (r = -0.83, P < 0.05) were negatively correlated with the glomerular filtration rate. Results of the receiver operating characteristic (ROC) curve analysis showed that corticomedullary APT values had high diagnostic efficacy in assessing different degrees of renal impairment.

CONCLUSIONS

The APT values of the cortex and medulla in patients with CKD gradually increased with disease progression. These findings indicated that APT imaging can be used to evaluate renal function and renal injury in patients with CKD.

Optimization of newly developed and lead shields thicknesses for protecting taxi drivers from 99mTc injected patients

Presently, public members are exposed to sources of ionizing radiation, and health risks due to radiation exposures should be a concern. This study aims to calculate the whole-body cumulative radiation exposure of taxi drivers. Also, this study will provide the effect of using a simple lead shield and three types of glass shield AVT6, TZN-D, and SLGC-E5, by calculating the effective annual dose of the taxi drivers that work in medical centers. Two MIRD phantoms as a driver and patient, a sample body of a taxi, pure lead, and glass sheets as a shield, were simulated using the MCNP code. We assumed that the patients had undergone the brain, liver, and kidney SPECT imaging by injecting 99mTC-HMPAO, 99mTC-sulfur colloid, and 99mTC-DMSA with the activity of 740MBq, 185MBq, and 333MBq, respectively. These shields are simulated on two sides of the driver, in the back and right side. The annual effective dose was calculated for 0-3.5 g/cm² area densities. It was observed that the 0.45, 1.09, 1.28, and 2.11 g/cm² of Pb, TZN-D, AVT6, and SLGC-E5 respectively decrease the effective dose below the allowed limit. According to the results, using the lead shield, the effective dose was reduced by a factor up to 7.25 times. It is recommended that taxi drivers wear a 0.4 mm lead shield or its equivalent when they have Tc-99 m injected patients.