Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains

Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients

BACKGROUND

The analytical renal pathology system (ARPS) based on convolutional neural networks has been used successfully in native IgA nephropathy (IgAN) patients. Considering the similarity of pathologic features, we aim to evaluate the performance of the ARPS in allograft IgAN patients and broaden its implementation.

METHODS

Biopsy-proven allograft IgAN patients from two different centers were enrolled for internal and external validation. We implemented the ARPS to identify glomerular lesions and intrinsic glomerular cells, and then evaluated its performance. Consistency between the ARPS and pathologists was assessed using intraclass correlation coefficients. The association of digital pathological features with clinical and pathological data was measured. Kaplan-Meier survival curve and cox proportional hazards model were applied to investigate prognosis prediction.

RESULTS

A total of 56 biopsy-proven allograft IgAN patients from the internal center and 17 biopsy-proven allograft IgAN patients from the external center were enrolled in this study. The ARPS was successfully applied to identify the glomerular lesions (F1-score, 0.696-0.959) and quantify intrinsic glomerular cells (F1-score, 0.888-0.968) in allograft IgAN patients rapidly and precisely. Furthermore, the mesangial hypercellularity score was positively correlated with all mesangial metrics provided by ARPS [Spearman's correlation coefficient (r), 0.439-0.472, and all p values < 0.001]. Besides, a higher allograft survival was noticed among patients in the high-level groups of the maximum and ratio of endothelial cells, as well as the maximum and density of podocytes.

CONCLUSION

We propose that the ARPS could be implemented in future clinical practice with outstanding capability.

Kidney medicine meets computer vision: a bibliometric analysis

BACKGROUND AND OBJECTIVE

Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research.

METHODS

The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer.

RESULTS

There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities.

CONCLUSION

The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.

Pediatric contributions and lessons learned from the NEPTUNE cohort study

Primary glomerular diseases are rare entities. This has hampered efforts to better understand the underlying pathobiology and to develop novel safe and effective therapies. NEPTUNE is a rare disease network that is focused on patients of all ages with minimal change disease, focal segmental glomerulosclerosis, and membranous nephropathy. It is a longitudinal cohort study that collects detailed demographic, clinical, histopathologic, genomic, transcriptomic, and metabolomic data. The goal is to develop a molecular classification for these disorders that supersedes the traditional pathological features-based schema. Pediatric patients are important contributors to this ongoing project. In this review, we provide a snapshot of the children and adolescents enrolled in NEPTUNE and summarize some key observations that have been made based on the data accumulated during the study. In addition, we describe the development of NEPTUNE Match, a program that aims to leverage the multi-scalar information gathered for each individual patient to provide guidance about potential clinical trial participation based on the molecular characterization and non-invasive biomarker profile. This represents the first organized effort to apply principles of precision medicine to the treatment of patients with primary glomerular disease. NEPTUNE has proven to be an invaluable asset in the study of glomerular diseases in patients of all ages including children and adolescents.

Decoding pathology: the role of computational pathology in research and diagnostics

Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS

Background

Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis.

Methods

Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset.

Results

N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA.

Conclusions

Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence

According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities. The increase in cancer mortality is due to limitations in the diagnosis and treatment options that are currently available. The close relationship between diagnostics and medicine has made it possible for cancer patients to receive precise diagnoses and individualized care. This article discusses newly developed compounds with potential for photodynamic therapy and diagnostic applications, as well as those already in use. In addition, it discusses the use of artificial intelligence in the analysis of diagnostic images obtained using, among other things, theranostic agents.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue

BACKGROUND

Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between biological structures. Statistical measures are insufficient for determining a model's viability and applicability in the diagnostic process. A deeper understanding of predictions is necessary in order to support histopathologists.

METHODS

We propose a method for providing supporting information in the form of segmentation of histological structures to histopathologists based on these principles. The proposed method utilizes nuclei type and density information in addition to standard image input provided at two different zoom levels for the semantic segmentation of blood vessels, inflammation, and endocardium in heart tissue.

RESULTS

The proposed method was able to reach state-of-the-art segmentation results. The overall quality and viability of the predictions was qualitatively evaluated by two pathologists and a histotechnologist.

CONCLUSIONS

The decision process of the proposed deep learning model utilizes the provided information sources correctly and simulates the decision process of histopathologists via the usage of a custom-designed attention gate that provides a combination of spatial and encoder attention mechanisms. The implementation is available at https://github.com/mathali/IEDL-segmentation-of-heart-tissue.

Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study

IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning. We collected retrospective data on demographic characteristics, blood tests, and urine tests of the patients who underwent kidney biopsy. The dataset was divided into derivation and validation cohorts, with temporal validation. We employed five machine learning models-eXtreme Gradient Boosting (XGBoost), LightGBM, Random Forest, Artificial Neural Networks, and 1 Dimentional-Convolutional Neural Network (1D-CNN)-and logistic regression, evaluating performance via the area under the receiver operating characteristic curve (AUROC) and explored variable importance through SHapley Additive exPlanations method. The study included 1268 participants, with 353 (28%) diagnosed with IgA nephropathy. In the derivation cohort, LightGBM achieved the highest AUROC of 0.913 (95% CI 0.906-0.919), significantly higher than logistic regression, Artificial Neural Network, and 1D-CNN, not significantly different from XGBoost and Random Forest. In the validation cohort, XGBoost demonstrated the highest AUROC of 0.894 (95% CI 0.850-0.935), maintaining its robust performance. Key predictors identified were age, serum albumin, IgA/C3, and urine red blood cells, aligning with existing clinical insights. Machine learning can be a valuable non-invasive tool for IgA nephropathy.

Deep learning applications for kidney histology analysis

PURPOSE OF REVIEW

Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives.

RECENT FINDINGS

Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios.

SUMMARY

Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.

Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy

BACKGROUND

Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed.

METHODS

A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed.

RESULTS

Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function.

CONCLUSION

Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors

Background

Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features using deep learning training to evaluate the response of patients with IMN to immunosuppressive therapy.

Methods

The 291 patients were randomly categorized into training (n = 219) and validation (n = 72) cohorts. Patch-level convolutional neural network training in a weakly supervised manner was utilized to analyze whole-slide histopathological features. We developed a machine-learning model to assess the predictive value of pathological signatures compared to clinical factors. The performance levels of the models were evaluated using the area under the receiver operating characteristic curve (AUC) on the training and validation tests, and the prediction accuracies of the models for immunotherapy response were compared.

Results

Multivariate analysis indicated that diabetes and smoking were independent risk factors affecting the response to immunotherapy in IMN patients. The model integrating pathologic features had a favorable predictive value for determining the response to immunotherapy in IMN patients, with AUCs of 0.85 and 0.77 when employed in the training and test cohorts, respectively. However, when incorporating clinical features into the model, the predictive efficacy diminishes, as evidenced by lower AUC values of 0.75 and 0.62 on the training and testing cohorts, respectively.

Conclusions

The model incorporating pathological signatures demonstrated a superior predictive ability for determining the response to immunosuppressive therapy in IMN patients compared to the integration of clinical factors.

Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies

Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

[Advances in computational quantitative nephropathology]

BACKGROUND

Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes.

AIM

To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions.

MATERIALS AND METHODS

Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design.

RESULTS AND DISCUSSION

Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.

A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization

Lesion scores on procurement donor biopsies are commonly used to guide organ utilization for deceased-donor kidneys. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropriate discard. Therefore, we constructed deep-learning based models to recognize kidney tissue compartments in hematoxylin & eosin-stained sections from procurement needle biopsies performed nationwide in years 2011-2020. To do this, we extracted whole-slide abnormality features from 2431 kidneys and correlated with pathologists' scores and transplant outcomes. A Kidney Donor Quality Score (KDQS) was derived and used in combination with recipient demographic and peri-transplant characteristics to predict graft loss or assist organ utilization. The performance on wedge biopsies was additionally evaluated. Our model identified 96% and 91% of normal/sclerotic glomeruli respectively; 94% of arteries/arterial intimal fibrosis; 90% of tubules. Whole-slide features of Sclerotic Glomeruli (GS)%, Arterial Intimal Fibrosis (AIF)%, and Interstitial Space Abnormality (ISA)% demonstrated strong correlations with corresponding pathologists' scores of all 2431 kidneys, but had superior associations with post-transplant estimated glomerular filtration rates in 2033 and graft loss in 1560 kidneys. The combination of KDQS and other factors predicted one- and four-year graft loss in a discovery set of 520 kidneys and a validation set of 1040 kidneys. By using the composite KDQS of 398 discarded kidneys due to "biopsy findings", we suggest that if transplanted, 110 discarded kidneys could have had similar survival to that of other transplanted kidneys. Thus, our composite KDQS and survival prediction models may facilitate risk stratification and organ utilization while potentially reducing unnecessary organ discard.

Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis

MOTIVATION

Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD).

RESULTS

We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ChunyueFeng/Kidney-DataSet.

Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions

INTRODUCTION

Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments.

METHODS

Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included.

RESULTS

Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification.

CONCLUSION

Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools.

Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology

Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been reused by other researchers so far, let alone employed in a clinical setting. To assess the current state of reproducibility and reusability of computational pathology algorithms, we evaluated peer-reviewed articles available in PubMed, published between January 2019 and March 2021, in 5 use cases: stain normalization; tissue type segmentation; evaluation of cell-level features; genetic alteration prediction; and inference of grading, staging, and prognostic information. We compiled criteria for data and code availability and statistical result analysis and assessed them in 160 publications. We found that only one-quarter (41 of 160 publications) made code publicly available. Among these 41 studies, three-quarters (30 of 41) analyzed their results statistically, half of them (20 of 41) released their trained model weights, and approximately a third (16 of 41) used an independent cohort for evaluation. Our review is intended for both pathologists interested in deep learning and researchers applying algorithms to computational pathology challenges. We provide a detailed overview of publications with published code in the field, list reusable data handling tools, and provide criteria for reproducibility and reusability.

Automated evaluation with deep learning of total interstitial inflammation and peritubular capillaritis on kidney biopsies

BACKGROUND

Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management.

METHODS

We used a convolutional neural network to evaluate those criteria on kidney biopsies. A total of 423 kidney samples from various diseases were included; 83 kidney samples were used for the neural network training, 106 for comparing manual annotations on limited areas to automated predictions, and 234 to compare automated and visual gradings.

RESULTS

The precision, recall and F-score for leukocyte detection were, respectively, 81%, 71% and 76%. Regarding peritubular capillaries detection the precision, recall and F-score were, respectively, 82%, 83% and 82%. There was a strong correlation between the predicted and observed grading of total inflammation, as for the grading of capillaritis (r = 0.89 and r = 0.82, respectively, all P < .0001). The areas under the receiver operating characteristics curves for the prediction of pathologists' Banff total inflammation (ti) and peritubular capillaritis (ptc) scores were respectively all above 0.94 and 0.86. The kappa coefficients between the visual and the neural networks' scores were respectively 0.74, 0.78 and 0.68 for ti ≥1, ti ≥2 and ti ≥3, and 0.62, 0.64 and 0.79 for ptc ≥1, ptc ≥2 and ptc ≥3. In a subgroup of patients with immunoglobulin A nephropathy, the inflammation severity was highly correlated to kidney function at biopsy on univariate and multivariate analyses.

CONCLUSION

We developed a tool using deep learning that scores the total inflammation and capillaritis, demonstrating the potential of artificial intelligence in kidney pathology.

Reconfiguration and loss of peritubular capillaries in chronic kidney disease

Functional and structural alterations of peritubular capillaries (PTCs) are a major determinant of chronic kidney disease (CKD). Using a software-based algorithm for semiautomatic segmentation and morphometric quantification, this study analyzes alterations of PTC shape associated with chronic tubulointerstitial injury in three mouse models and in human biopsies. In normal kidney tissue PTC shape was predominantly elongated, whereas the majority of PTCs associated with chronic tubulointerstitial injury had a rounder shape. This was reflected by significantly reduced PTC luminal area, perimeter and diameters as well as by significantly increased circularity and roundness. These morphological alterations were consistent in all mouse models and human kidney biopsies. The mean circularity of PTCs correlated significantly with categorized glomerular filtration rates and the degree of interstitial fibrosis and tubular atrophy (IFTA) and classified the presence of CKD or IFTA. 3D reconstruction of renal capillaries revealed not only a significant reduction, but more importantly a substantial simplification and reconfiguration of the renal microvasculature in mice with chronic tubulointerstitial injury. Computational modelling predicted that round PTCs can deliver oxygen more homogeneously to the surrounding tissue. Our findings indicate that alterations of PTC shape represent a common and uniform reaction to chronic tubulointerstitial injury independent of the underlying kidney disease.

Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators

Diagnosis of diabetic kidney disease (DKD) mainly relies on screening the morphological variations and internal lesions of glomeruli from pathological kidney biopsy. The prominent pathological alterations of glomeruli for DKD include glomerular hypertrophy and nodular mesangial sclerosis. However, the qualitative judgment of these alterations is inaccurate and inconstant due to the intra- and inter-subject variability of pathologists. It is necessary to design artificial intelligence (AI) methods for accurate quantification of these pathological alterations and outcome prediction of DKD. In this work, we present an AI-driven framework to quantify the volume of glomeruli and degree of nodular mesangial sclerosis, respectively, based on an instance segmentation module and a novel weakly supervised Macro-Micro Aggregation (MMA) module. Subsequently, we construct classic machine learning models to predict the degree of DKD based on three selected pathological indicators via factor analysis. These corresponding modules are trained and tested on a total of 281 whole slide images (WSIs) digitized from two hospitals with different scanners. Our designed AI framework achieved inspiring results with 0.926 mIoU for glomerulus segmentation, and 0.899 F1 score for glomerulus classification in the external testing dataset. Meantime, the visualized results of the MMA module could reflect the location of the lesions. The performance of predicting disease achieved the F1 score of 0.917, which further proved the effectiveness of our AI-driven quantification of pathological indicators. Additionally, the interpretation of the machine learning model with the SHAP method showed similar accordance with the development of DKD in pathology. In conclusion, the proposed auxiliary diagnostic technologies have the feasibility for quantitative analysis of glomerular pathological tissues and alterations in DKD. Pathological quantitative indicators will also make it more convenient to provide doctors with assistance in clinical practice.

A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images

Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity.

MATERIALS AND METHODS

We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps.

RESULTS

A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers.

CONCLUSIONS

We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.

Machine learning-based intradialytic hypotension prediction of patients undergoing hemodialysis: A multicenter retrospective study

BACKGROUND AND OBJECTIVE

Intradialytic hypotension (IDH) is closely associated with adverse clinical outcomes in HD-patients. An IDH predictor model is important for IDH risk screening and clinical decision-making. In this study, we used Machine learning (ML) to develop IDH model for risk prediction in HD patients.

METHODS

62,227 dialysis sessions were randomly partitioned into training data (70%), test data (20%), and validation data (10%). IDH-A model based on twenty-seven variables was constructed for risk prediction for the next HD treatment. IDH-B model based on ten variables from 64,870 dialysis sessions was developed for risk assessment before each HD treatment. Light Gradient Boosting Machine (LightGBM), Linear Discriminant Analysis, support vector machines, XGBoost, TabNet, and multilayer perceptron were used to develop the predictor model.

RESULTS

In IDH-A model, we identified the LightGBM method as the best-performing and interpretable model with C- statistics of 0.82 in Fall30Nadir90 definitions, which was higher than those obtained using the other models (P<0.01). In other IDH standards of Nadir90, Nadir100, Fall20, Fall30, and Fall20Nadir90, the LightGBM method had a performance with C- statistics ranged 0.77 to 0.89. As a complementary application, the LightGBM model in IDH-B model achieved C- statistics of 0.68 in Fall30Nadir90 definitions and 0.69 to 0.78 in the other five IDH standards, which were also higher than the other methods, respectively.

CONCLUSION

Use ML, we identified the LightGBM method as the good-performing and interpretable model. We identified the top variables as the high-risk factors for IDH incident in HD-patient. IDH-A and IDH-B model can usefully complement each other for risk prediction and further facilitate timely intervention through applied into different clinical setting.

Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology

This study focuses on ischaemia-reperfusion injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a convolutional neural network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular casts and Tubular necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman's rank correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.

A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice

BACKGROUND

Diagnosis and staging of diabetic kidney disease (DKD) via the serial assessment of routine laboratory indices lacks the granularity required to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD.

SUMMARY

Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Multi-centre prospective cohort studies, including the Kidney Precision Medicine Project (KPMP) and the European Nephrectomy Biobank (ENBiBA) project, will improve access to human diabetic kidney tissue for research purposes. Integration of diverse data domains from such initiatives including clinical phenotypic data, renal and retinal imaging biomarkers, histopathological and ultrastructural data, and an array of molecular omics (transcriptomics, proteomics, etc.) alongside multi-dimensional data from preclinical modelling offers exciting opportunities to unravel individual-level mechanisms underlying progressive DKD. The application of machine and deep learning approaches may particularly enhance insights derived from imaging and histopathological/ultrastructural data domains.

KEY MESSAGES

Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.

Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation

Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this article, we propose the Omni-Seg network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5× to 40× scale) pathological image segmentation via a single neural network. The contribution of this article is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from 150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics).

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.

Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

Background

Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections.

Methods

A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the relationship of histomorphometric parameters with age, sex, and SCr.

Results

Our deep-learning model achieved high segmentation performance for all test compartments. The size and density of nephrons and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Nephron size was significantly dependent on SCr. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of age.

Conclusions

Using deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics and SCr. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis.

Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies

Key Points

Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when measured in interstitial fractional space. PTC shape (aspect ratio) is associated with clinical outcome in glomerular diseases.

Background

The association between peritubular capillary (PTC) density and disease progression has been studied in a variety of kidney diseases using immunohistochemistry. However, other PTC attributes, such as PTC shape, have not been explored yet. The recent development of computer vision techniques provides the opportunity for the quantification of PTC attributes using conventional stains and whole-slide images.

Methods

To explore the relationship between PTC characteristics and clinical outcome, n =280 periodic acid–Schiff-stained kidney biopsies (88 minimal change disease, 109 focal segmental glomerulosclerosis, 46 membranous nephropathy, and 37 IgA nephropathy) from the Nephrotic Syndrome Study Network digital pathology repository were computationally analyzed. A previously validated deep learning model was applied to segment cortical PTCs. Average PTC aspect ratio (PTC major to minor axis ratio), size (PTC pixels per PTC segmentation), and density (PTC pixels per unit cortical area) were computed for each biopsy. Cox proportional hazards models were used to assess associations between these PTC parameters and outcome (40% eGFR decline or kidney failure). Cortical PTC characteristics and interstitial fractional space PTC density were compared between areas of interstitial fibrosis and tubular atrophy (IFTA) and areas without IFTA.

Results

When normalized PTC aspect ratio was below 0.6, a 0.1, increase in normalized PTC aspect ratio was significantly associated with disease progression, with a hazard ratio (95% confidence interval) of 1.28 (1.04 to 1.59) (P = 0.019), while PTC density and size were not significantly associated with outcome. Interstitial fractional space PTC density was lower in areas of IFTA compared with non-IFTA areas.

Conclusions

Computational image analysis enables quantification of the status of the kidney microvasculature and the discovery of a previously unrecognized PTC biomarker (aspect ratio) of clinical outcome.

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).

Machine learning classification of placental villous infarction, perivillous fibrin deposition, and intervillous thrombus

INTRODUCTION

Placental parenchymal lesions are commonly encountered and carry significant clinical associations. However, they are frequently missed or misclassified by general practice pathologists. Interpretation of pathology slides has emerged as one of the most successful applications of machine learning (ML) in medicine with applications ranging from cancer detection and prognostication to transplant medicine. The goal of this study was to use a whole-slide learning model to identify and classify placental parenchymal lesions including villous infarctions, intervillous thrombi (IVT), and perivillous fibrin deposition (PVFD).

METHODS

We generated whole slide images from placental discs examined at our institution with infarct, IVT, PVFD, or no macroscopic lesion. Slides were analyzed as a set of overlapping patches. We extracted feature vectors from each patch using a pretrained convolutional neural network (EfficientNetV2L). We trained a model to assign attention to each vector and used the attentions as weights to produce a pooled feature vector. The pooled vector was classified as normal or 1 of 3 lesions using a fully connected network. Patch attention was plotted to highlight informative areas of the slide.

RESULTS

Overall balanced accuracy in a test set of held-out slides was 0.86 with receiver-operator characteristic areas under the curve of 0.917-0.993. Cases of PVFD were frequently miscalled as normal or infarcts, the latter possibly due to the perivillous fibrin found at the periphery of infarctions. We used attention maps to further understand some errors, including one most likely due to poor tissue fixation and processing.

DISCUSSION

We used a whole-slide learning paradigm to train models to recognize three of the most common placental parenchymal lesions. We used attention maps to gain insight into model function, which differed from intuitive explanations.

Deep learning-based segmentation and quantification of podocyte foot process morphology suggests differential patterns of foot process effacement across kidney pathologies

Morphological alterations at the kidney filtration barrier increase intrinsic capillary wall permeability resulting in albuminuria. However, automated, quantitative assessment of these morphological changes has not been possible with electron or light microscopy. Here we present a deep learning-based approach for segmentation and quantitative analysis of foot processes in images acquired with confocal and super-resolution fluorescence microscopy. Our method, Automatic Morphological Analysis of Podocytes (AMAP), accurately segments podocyte foot processes and quantifies their morphology. AMAP applied to a set of kidney diseases in patient biopsies and a mouse model of focal segmental glomerulosclerosis allowed for accurate and comprehensive quantification of various morphometric features. With the use of AMAP, detailed morphology of podocyte foot process effacement was found to differ between categories of kidney pathologies, showed detailed variability between diverse patients with the same clinical diagnosis, and correlated with levels of proteinuria. AMAP could potentially complement other readouts such as various omics, standard histologic/electron microscopy and blood/urine assays for future personalized diagnosis and treatment of kidney disease. Thus, our novel finding could have implications to afford an understanding of early phases of kidney disease progression and may provide supplemental information in precision diagnostics.

An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level

Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological segmentation with less computational complexity. However, the patch-wise segmentation paradigm still applies to Omni-Seg, and the pipeline is time-consuming when providing segmentation for Whole Slide Images (WSIs). In this paper, we propose an enhanced version of the Omni-Seg pipeline in order to reduce the repetitive computing processes and utilize a GPU to accelerate the model's prediction for both better model performance and faster speed. Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time. The proposed accelerated implementation reduced the average processing time (at the testing stage) on a standard needle biopsy WSI from 2.3 hours to 22 minutes, using 35 WSIs from the Kidney Tissue Atlas (KPMP) Datasets. The source code and the Docker have been made publicly available at https://github.com/ddrrnn123/Omni-Seg.

Quantitative scoring of progression in transplant glomerulopathy using digital pathology may be superior to Banff cg scoring

Antibody-mediated rejection (ABMR) is a major cause of kidney allograft failure. Biopsy-based surrogate endpoints reflecting ABMR progression on sequential biopsies that predict long-term outcome offer the potential to make treatment trials for ABMR feasible. However, the Banff transplant glomerulopathy (TG) scoring system (chronic glomerular injury score [cg]) relies on relatively crude and arbitrary ordinal grades and has low inter-observer concordance that currently limits its usefulness as a surrogate endpoint for ABMR progression in clinical drug trials. Here, we describe and validate a novel quantitative method for quantifying progression of TG in ABMR. Using digital pathology in sequential biopsies from 75 patients at various stages of ABMR, we scored all capillaries in the most affected glomeruli for basement membrane duplication that were correlated with allograft function, outcome, Banff lesion scores, and gene expression. Our digital scoring reflected TG progression better than the categorical Banff cg score and correlated with Banff ABMR and chronicity lesions, but not transcript changes. In multivariate analysis, the delta change between biopsies with serum creatinine and mean percent duplicated glomerular basement membranes was significantly associated with graft loss. Neither the delta in any Banff lesion scores (including cg) nor in gene expression was associated with outcome. Receiver operating characteristic curve analysis showed that the digital pathology approach was superior to the conventional score for predicting graft failure. Thus, our digital pathology-based approach for scoring TG accurately assessed progression in TG. However, further validation as a potential surrogate endpoint in clinical trials for the treatment of ABMR is warranted.

Next-Generation Morphometry for pathomics-data mining in histopathology

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the "Hacking the Human Body" machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We showcase how 1,175 teams from 78 countries engaged in community- driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

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.

Stain-Independent Deep Learning-Based Analysis of Digital Kidney Histopathology

Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain augmentation is proposed to develop stain-independent CNNs requiring only one annotated stain. In this benchmark study on stain independence in digital pathology, this approach is comprehensively compared with state-of-the-art techniques including image registration and stain translation, and several modifications thereof. A previously developed CNN for segmentation of periodic acid-Schiff-stained kidney histology was used and applied to various immunohistochemical stainings. Stain augmentation showed very high performance in all evaluated stains and outperformed all other techniques in all structures and stains. Without the need for additional annotations, it enabled segmentation on immunohistochemical stainings with performance nearly comparable to that of the annotated periodic acid-Schiff stain and could further uphold performance on several held-out stains not seen during training. Herein, examples of how this framework can be applied for compartment-specific quantification of immunohistochemical stains for inflammation and fibrosis in animal models and patient biopsy specimens are presented. The results show that stain augmentation is a highly effective approach to enable stain-independent applications of deep-learning segmentation algorithms. This opens new possibilities for broad implementation in digital pathology.

Artificial intelligence in glomerular diseases

In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease (CKD). With the development of natural language processing, the clinical history of a patient can be used to identify a computable phenotype. In kidney pathology, digital imaging has adopted innovative deep learning algorithms (DLAs) that can improve the predictive capability of the examined lesions. However, at this time, these applications can only be used in research because there is no recognized validation to replace the conventional diagnostic applications. Kidney ultrasonography, used in the clinical examination of patients, provides information about the progression of kidney damage. Machine learning algorithms (MLAs) with promising results for the early detection of CKD have been proposed, but, still, they are not solid enough to be incorporated into the clinical practice. A few tools for glomerulonephritis, based on MLAs, are available in clinical practice. They can be downloaded on computers and cellular phones but can only be applied to uniracial cohorts of patients. To improve their performance, it is necessary to organize large consortia with multiracial cohorts. Finally, in many studies MLA development has been carried out using retrospective cohorts. The performance of the models might differ in retrospective cohorts compared to real-world data. Therefore, the models should be validated in prospective external large cohorts.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3⁺ cell density within scarred regions and higher CD3⁺ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.

Prognostic Implications of a Morphometric Evaluation for Chronic Changes on All Diagnostic Native Kidney Biopsies

BACKGROUND

Semiquantitative visual inspection for glomerulosclerosis, interstitial fibrosis, and arteriosclerosis is often used to assess chronic changes in native kidney biopsies. Morphometric evaluation of these and other chronic changes may improve the prognostic assessment.

METHODS

We studied a historical cohort of patients who underwent a native kidney biopsy between 1993 and 2015 and were followed through 2021 for ESKD and for progressive CKD (defined as experiencing 50% eGFR decline, temporary dialysis, or ESKD). Pathologist scores for the percentages of globally sclerosed glomeruli (GSG), interstitial fibrosis and tubular atrophy (IFTA), and arteriosclerosis (luminal stenosis) were available. We scanned biopsy sections into high-resolution images to trace microstructures. Morphometry measures were percentage of GSG; percentage of glomerulosclerosis (percentage of GSG, ischemic-appearing glomeruli, or segmentally sclerosed glomeruli); percentage of IFTA; IFTA foci density; percentage of artery luminal stenosis; arteriolar hyalinosis counts; and measures of nephron size. Models assessed risk of ESKD or progressive CKD with biopsy measures adjusted for age, hypertension, diabetes, body mass index, eGFR, and proteinuria.

RESULTS

Of 353 patients (followed for a median 7.5 years), 75 developed ESKD and 139 experienced progressive CKD events. Visually estimated scores by pathologists versus morphometry measures for percentages of GSG, IFTA, and luminal stenosis did not substantively differ in predicting outcomes. However, adding percentage of glomerulosclerosis, IFTA foci density, and arteriolar hyalinosis improved outcome prediction. A 10-point score using percentage of glomerulosclerosis, percentage of IFTA, IFTA foci density, and any arteriolar hyalinosis outperformed a 10-point score based on percentages of GSG, IFTA, and luminal stenosis >50% in discriminating risk of ESKD or progressive CKD.

CONCLUSION

Morphometric characterization of glomerulosclerosis, IFTA, and arteriolar hyalinosis on kidney biopsy improves prediction of long-term kidney outcomes.

Multi-modality artificial intelligence in digital pathology

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.

Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization

The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures in the tissue. Considering the glomerulus as one of the first structures analyzed by pathologists, we propose here a novel convolutional neural network for glomerulus segmentation. Our end-to-end network, named DS-FNet, combines the strengths of semantic segmentation and semantic boundary detection networks via an attention-aware mechanism. Although we trained the proposed network on periodic acid-Schiff (PAS)-stained WSIs, we found that our network was capable to segment glomeruli on WSIs stained with different techniques, such as periodic acid-methenamine silver (PAMS), hematoxylin-eosin (HE), and Masson trichrome (TRI). To assess the performance of the proposed method, we used three public data sets: HuBMAP (available in a Kaggle competition), a subset of the NEPTUNE data set, and a novel challenging data set, called WSI_Fiocruz. We compared the DS-FNet with six other deep learning networks: original U-Net, our attention version of U-Net called AU-Net, U-Net++, U-Net3Plus, ResU-Net, and DeepLabV3+. Results showed that DS-FNet achieved equivalent or superior results on all data sets: On the HuBMAP data set, it reached a dice score (DSC) of 95.05%, very close to the first place (95.15%); on the NEPTUNE and WSI_Fiocruz data sets, DS-FNet obtained the highest average DSC, whether on PAS-stained images or images stained with other techniques. To the best we know, this is the first work to show consistently high performance in a one-to-many-stain glomerulus segmentation following a thorough protocol on data sets from different medical labs.

Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology

We reviewed some of the latest advancements in the use of mathematical models in nephrology. We looked over 2 distinct categories of mathematical models that are widely used in biological research and pointed out some of their strengths and weaknesses when applied to health care, especially in the context of nephrology. A mechanistic dynamical system allows the representation of causal relations among the system variables but with a more complex and longer development/implementation phase. Artificial intelligence/machine learning provides predictive tools that allow identifying correlative patterns in large data sets, but they are usually harder-to-interpret black boxes. Chronic kidney disease (CKD), a major worldwide health problem, generates copious quantities of data that can be leveraged by choice of the appropriate model; also, there is a large number of dialysis parameters that need to be determined at every treatment session that can benefit from predictive mechanistic models. Following important steps in the use of mathematical methods in medical science might be in the intersection of seemingly antagonistic frameworks, by leveraging the strength of each to provide better care.

A deep learning-based automatic staging method for early endometrial cancer on MRI images

Early treatment increases the 5-year survival rate of patients with endometrial cancer (EC). Deep learning (DL) as a new computer-aided diagnosis method has been widely used in medical image processing which can reduce the misdiagnosis by radiologists. An automatic staging method based on DL for the early diagnosis of EC will benefit both radiologists and patients. To develop an effective and automatic prediction model for early EC diagnosis on magnetic resonance imaging (MRI) images, we retrospectively enrolled 117 patients (73 of stage IA, 44 of stage IB) with a pathological diagnosis of early EC confirmed by postoperative biopsy at our institution from 1 January 2018, to 31 December 2020. Axial T2-weighted image (T2WI), axial diffusion-weighted image (DWI) and sagittal T2WI images from 117 patients have been classified into stage IA and stage IB according to the patient’s pathological diagnosis. Firstly, a semantic segmentation model based on the U-net network is trained to segment the uterine region and the tumor region on the MRI images. Then, the area ratio of the tumor region to the uterine region (TUR) in the segmentation map is calculated. Finally, the receiver operating characteristic curves (ROCs) are plotted by the TUR and the results of the patient’s pathological diagnosis in the test set to find the optimal staging thresholds for stage IA and stage IB. In the test sets, the trained semantic segmentation model yields the average Dice similarity coefficients of uterus and tumor on axial T2WI, axial DWI, and sagittal T2WI were 0.958 and 0.917, 0.956 and 0.941, 0.972 and 0.910 respectively. With pathological diagnostic results as the gold standard, the classification model on axial T2WI, axial DWI, and sagittal T2WI yielded an area under the curve (AUC) of 0.86, 0.85 and 0.94, respectively. In this study, an automatic DL-based segmentation model combining the ROC analysis of TUR on MRI images presents an effective early EC staging method.