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Hölscher DL, Goedertier M, Klinkhammer BM, Droste P, Costa IG, Boor P, Bülow RD. tRigon: an R package and Shiny App for integrative (path-)omics data analysis. BMC Bioinformatics 2024; 25:98. [PMID: 38443821 PMCID: PMC10916305 DOI: 10.1186/s12859-024-05721-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis. RESULTS tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon. CONCLUSIONS tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.
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Affiliation(s)
- David L Hölscher
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Michael Goedertier
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Patrick Droste
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany.
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2
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Farris AB, Alexander MP, Balis UGJ, Barisoni L, Boor P, Bülow RD, Cornell LD, Demetris AJ, Farkash E, Hermsen M, Hogan J, Kain R, Kers J, Kong J, Levenson RM, Loupy A, Naesens M, Sarder P, Tomaszewski JE, van der Laak J, van Midden D, Yagi Y, Solez K. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments. Transpl Int 2023; 36:11783. [PMID: 37908675 PMCID: PMC10614670 DOI: 10.3389/ti.2023.11783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/22/2023] [Indexed: 11/02/2023]
Abstract
The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.
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Affiliation(s)
- Alton B. Farris
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GE, United States
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ulysses G. J. Balis
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Laura Barisoni
- Department of Pathology and Medicine, Duke University, Durham, NC, United States
| | - Peter Boor
- Institute of Pathology, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University Clinic, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Roman D. Bülow
- Institute of Pathology, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University Clinic, Aachen, Germany
| | - Lynn D. Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Anthony J. Demetris
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Evan Farkash
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Julien Hogan
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GE, United States
- Nephrology Service, Robert Debré Hospital, University of Paris, Paris, France
| | - Renate Kain
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Jesper Kers
- Department of Pathology, Amsterdam University Medical Centers, Amsterdam, Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
| | - Jun Kong
- Georgia State University, Atlanta, GA, United States
- Emory University, Atlanta, GA, United States
| | - Richard M. Levenson
- Department of Pathology, University of California Davis Health System, Sacramento, CA, United States
| | - Alexandre Loupy
- Institut National de la Santé et de la Recherche Médicale, UMR 970, Paris Translational Research Centre for Organ Transplantation, and Kidney Transplant Department, Hôpital Necker, Assistance Publique-Hôpitaux de Paris, University of Paris, Paris, France
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Pinaki Sarder
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, Intelligent Critical Care Center, College of Medicine, University of Florida at Gainesville, Gainesville, FL, United States
| | - John E. Tomaszewski
- Department of Pathology, The State University of New York at Buffalo, Buffalo, NY, United States
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Dominique van Midden
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Yukako Yagi
- Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kim Solez
- Department of Pathology, University of Alberta, Edmonton, AB, Canada
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3
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Klinkhammer BM, Boor P. Kidney fibrosis: Emerging diagnostic and therapeutic strategies. Mol Aspects Med 2023; 93:101206. [PMID: 37541106 DOI: 10.1016/j.mam.2023.101206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/25/2023] [Indexed: 08/06/2023]
Abstract
An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.
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Affiliation(s)
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany; Division of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany.
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4
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Hölscher DL, Bouteldja N, Joodaki M, Russo ML, Lan YC, Sadr AV, Cheng M, Tesar V, Stillfried SV, Klinkhammer BM, Barratt J, Floege J, Roberts ISD, Coppo R, Costa IG, Bülow RD, Boor P. Next-Generation Morphometry for pathomics-data mining in histopathology. Nat Commun 2023; 14:470. [PMID: 36709324 PMCID: PMC9884209 DOI: 10.1038/s41467-023-36173-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/16/2023] [Indexed: 01/29/2023] Open
Abstract
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.
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Affiliation(s)
- David L Hölscher
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Nassim Bouteldja
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Mehdi Joodaki
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Yu-Chia Lan
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Mingbo Cheng
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Vladimir Tesar
- Department of Nephrology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | | | | | - Jonathan Barratt
- John Walls Renal Unit, University Hospital of Leicester National Health Service Trust, Leicester, United Kingdom
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Jürgen Floege
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Ian S D Roberts
- Department of Cellular Pathology, Oxford University Hospitals National Health Services Foundation Trust, Oxford, United Kingdom
| | - Rosanna Coppo
- Fondazione Ricerca Molinette, Torino, Italy
- Regina Margherita Children's University Hospital, Torino, Italy
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany.
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany.
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5
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Stain-Independent Deep Learning-Based Analysis of Digital Kidney Histopathology. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:73-83. [PMID: 36309103 DOI: 10.1016/j.ajpath.2022.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/10/2022] [Accepted: 09/30/2022] [Indexed: 11/12/2022]
Abstract
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.
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6
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Bai Q, Su C, Tang W, Li Y. Machine learning to predict end stage kidney disease in chronic kidney disease. Sci Rep 2022; 12:8377. [PMID: 35589908 PMCID: PMC9120106 DOI: 10.1038/s41598-022-12316-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 05/09/2022] [Indexed: 12/28/2022] Open
Abstract
The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.
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Affiliation(s)
- Qiong Bai
- Department of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China
| | - Chunyan Su
- Department of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China
| | - Wen Tang
- Department of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People's Republic of China.
| | - Yike Li
- Department of Otolaryngology-Head and Neck Surgery, Bill Wilkerson Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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7
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Moeller MJ, Kramann R, Lammers T, Hoppe B, Latz E, Ludwig-Portugall I, Boor P, Floege J, Kurts C, Weiskirchen R, Ostendorf T. New Aspects of Kidney Fibrosis-From Mechanisms of Injury to Modulation of Disease. Front Med (Lausanne) 2022; 8:814497. [PMID: 35096904 PMCID: PMC8790098 DOI: 10.3389/fmed.2021.814497] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 12/20/2021] [Indexed: 02/02/2023] Open
Abstract
Organ fibrogenesis is characterized by a common pathophysiological final pathway independent of the underlying progressive disease of the respective organ. This makes it particularly suitable as a therapeutic target. The Transregional Collaborative Research Center “Organ Fibrosis: From Mechanisms of Injury to Modulation of Disease” (referred to as SFB/TRR57) was hosted from 2009 to 2021 by the Medical Faculties of RWTH Aachen University and the University of Bonn. This consortium had the ultimate goal of discovering new common but also different fibrosis pathways in the liver and kidneys. It finally successfully identified new mechanisms and established novel therapeutic approaches to interfere with hepatic and renal fibrosis. This review covers the consortium's key kidney-related findings, where three overarching questions were addressed: (i) What are new relevant mechanisms and signaling pathways triggering renal fibrosis? (ii) What are new immunological mechanisms, cells and molecules that contribute to renal fibrosis?, and finally (iii) How can renal fibrosis be modulated?
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Affiliation(s)
- Marcus J Moeller
- Division of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany.,Heisenberg Chair for Preventive and Translational Nephrology, Aachen, Germany
| | - Rafael Kramann
- Division of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany.,Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Hospital, Aachen, Germany.,Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, Netherlands
| | - Twan Lammers
- Department of Nanomedicine and Theranostics, Faculty of Medicine, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Bernd Hoppe
- Division of Pediatric Nephrology and Kidney Transplantation, University Hospital of Bonn, Bonn, Germany.,German Hyperoxaluria Center, Pediatric Kidney Care Center, Bonn, Germany
| | - Eicke Latz
- Institute of Innate Immunity, University Hospital of Bonn, Bonn, Germany
| | - Isis Ludwig-Portugall
- Institute for Molecular Medicine and Experimental Immunology, University Hospital of Bonn, Bonn, Germany
| | - Peter Boor
- Division of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany.,Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Jürgen Floege
- Division of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Christian Kurts
- Institute for Molecular Medicine and Experimental Immunology, University Hospital of Bonn, Bonn, Germany.,Department of Microbiology and Immunology, Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Ralf Weiskirchen
- Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry (IFMPEGKC), University Hospital RWTH Aachen, Aachen, Germany
| | - Tammo Ostendorf
- Division of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany
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8
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Kers J, Bülow RD, Klinkhammer BM, Breimer GE, Fontana F, Abiola AA, Hofstraat R, Corthals GL, Peters-Sengers H, Djudjaj S, von Stillfried S, Hölscher DL, Pieters TT, van Zuilen AD, Bemelman FJ, Nurmohamed AS, Naesens M, Roelofs JJTH, Florquin S, Floege J, Nguyen TQ, Kather JN, Boor P. Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit Health 2021; 4:e18-e26. [PMID: 34794930 DOI: 10.1016/s2589-7500(21)00211-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/10/2021] [Accepted: 08/23/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection. METHODS We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance). FINDINGS Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0·87 [ten times bootstrapped CI 0·85-0·88]) and disease (0·87 [0·86-0·88]), followed by a second CNN classifying biopsies classified as disease into rejection (0·75 [0·73-0·76]) and other diseases (0·75 [0·72-0·77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0·83 [0·80-0·85], disease 0·83 [0·73-0·91]; second CNN rejection 0·61 [0·51-0·70], other diseases 0·61 [0·50-0·74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0·80 [0·73-0·84], rejection 0·76 [0·66-0·80], other diseases 0·50 [0·36-0·57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium. INTERPRETATION This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. FUNDING European Research Council; German Research Foundation; German Federal Ministries of Education and Research, Health, and Economic Affairs and Energy; Dutch Kidney Foundation; Human(e) AI Research Priority Area of the University of Amsterdam; and Max-Eder Programme of German Cancer Aid.
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Affiliation(s)
- Jesper Kers
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Department of Pathology, Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands; Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Netherlands.
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | | | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Francesco Fontana
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Nephrology and Dialysis Unit, University Hospital of Modena, Modena, Italy
| | - Adeyemi Adefidipe Abiola
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Department of Morbid Anatomy and Forensic Medicine, Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, Nigeria
| | - Rianne Hofstraat
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Garry L Corthals
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Hessel Peters-Sengers
- Center for Experimental and Molecular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Sonja Djudjaj
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | | | - David L Hölscher
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Tobias T Pieters
- Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands
| | - Arjan D van Zuilen
- Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands
| | - Frederike J Bemelman
- Renal Transplant Unit, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Azam S Nurmohamed
- Renal Transplant Unit, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Maarten Naesens
- Department of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium; Department of Microbiology, Immunology, and Transplantation, KU Leuven, Leuven, Belgium
| | - Joris J T H Roelofs
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Sandrine Florquin
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Jürgen Floege
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Tri Q Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jakob N Kather
- Department of Medicine III, RWTH Aachen University Hospital, Aachen, Germany; Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Boor
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany; Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
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9
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Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010798. [PMID: 34682567 PMCID: PMC8535636 DOI: 10.3390/ijerph182010798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/24/2021] [Accepted: 09/26/2021] [Indexed: 12/14/2022]
Abstract
Chronic kidney disease is one of the most important causes of mortality worldwide, but a shortage of nephrology pathologists has led to delays or errors in its diagnosis and treatment. Immunofluorescence (IF) images of patients with IgA nephropathy (IgAN), membranous nephropathy (MN), diabetic nephropathy (DN), and lupus nephritis (LN) were obtained from the General Hospital of Chinese PLA. The data were divided into training and test data. To simulate the inaccurate focus of the fluorescence microscope, the Gaussian method was employed to blur the IF images. We proposed a novel multi-task learning (MTL) method for image quality assessment, de-blurring, and disease classification tasks. A total of 1608 patients’ IF images were included—1289 in the training set and 319 in the test set. For non-blurred IF images, the classification accuracy of the test set was 0.97, with an AUC of 1.000. For blurred IF images, the proposed MTL method had a higher accuracy (0.94 vs. 0.93, p < 0.01) and higher AUC (0.993 vs. 0.986) than the common MTL method. The novel MTL method not only diagnosed four types of kidney diseases through blurred IF images but also showed good performance in two auxiliary tasks: image quality assessment and de-blurring.
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Bülow RD, Dimitrov D, Boor P, Saez-Rodriguez J. How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade? Semin Immunopathol 2021; 43:739-752. [PMID: 33835214 PMCID: PMC8551101 DOI: 10.1007/s00281-021-00847-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 02/17/2021] [Indexed: 01/16/2023]
Abstract
IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.
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Affiliation(s)
- Roman David Bülow
- University Hospital RWTH Aachen, Institute of Pathology, Aachen, Germany
| | - Daniel Dimitrov
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany
- Institute for Computational Biomedicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Peter Boor
- University Hospital RWTH Aachen, Institute of Pathology, Aachen, Germany.
- Department of Nephrology and Immunology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University, Heidelberg, Germany.
- Institute for Computational Biomedicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany.
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), 52074, RWTH Aachen University, Aachen, Germany.
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany.
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11
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Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology 2021; 78:791-804. [PMID: 33211332 DOI: 10.1111/his.14304] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.
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Affiliation(s)
- Alton B Farris
- Department of Pathology and Laboratory Medicine, Atlanta, GA, USA
| | - Juan Vizcarra
- Department of Bioinformatics, Emory University, Atlanta, GA, USA
| | - Mohamed Amgad
- Department of Pathology and Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA
| | - Lee A D Cooper
- Department of Pathology and Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA
| | - David Gutman
- Department of Bioinformatics, Emory University, Atlanta, GA, USA
| | - Julien Hogan
- Department of Surgery, Emory University, Atlanta, GA, USA
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12
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Stauss M, Floyd L, Becker S, Ponnusamy A, Woywodt A. Opportunities in the cloud or pie in the sky? Current status and future perspectives of telemedicine in nephrology. Clin Kidney J 2021; 14:492-506. [PMID: 33619442 PMCID: PMC7454484 DOI: 10.1093/ckj/sfaa103] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Indexed: 12/15/2022] Open
Abstract
The use of telehealth to support, enhance or substitute traditional methods of delivering healthcare is becoming increasingly common in many specialties, such as stroke care, radiology and oncology. There is reason to believe that this approach remains underutilized within nephrology, which is somewhat surprising given the fact that nephrologists have always driven technological change in developing dialysis technology. Despite the obvious benefits that telehealth may provide, robust evidence remains lacking and many of the studies are anecdotal, limited to small numbers or without conclusive proof of benefit. More worryingly, quite a few studies report unexpected obstacles, pitfalls or patient dissatisfaction. However, with increasing global threats such as climate change and infectious disease, a change in approach to delivery of healthcare is needed. The current pandemic with coronavirus disease 2019 (COVID-19) has prompted the renal community to embrace telehealth to an unprecedented extent and at speed. In that sense the pandemic has already served as a disruptor, changed clinical practice and shown immense transformative potential. Here, we provide an update on current evidence and use of telehealth within various areas of nephrology globally, including the fields of dialysis, inpatient care, virtual consultation and patient empowerment. We also provide a brief primer on the use of artificial intelligence in this context and speculate about future implications. We also highlight legal aspects and pitfalls and discuss the 'digital divide' as a key concept that healthcare providers need to be mindful of when providing telemedicine-based approaches. Finally, we briefly discuss the immediate use of telenephrology at the onset of the COVID-19 pandemic. We hope to provide clinical nephrologists with an overview of what is currently available, as well as a glimpse into what may be expected in the future.
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Affiliation(s)
- Madelena Stauss
- Department of Renal Medicine, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Lauren Floyd
- Department of Renal Medicine, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Stefan Becker
- DaVita Dialysis Centre Duisburg, Duisburg, Germany
- Department of Nephrology, University Hospital Essen, Essen, Germany
| | - Arvind Ponnusamy
- Department of Renal Medicine, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Alexander Woywodt
- Department of Renal Medicine, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
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13
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Bülow RD, Kers J, Boor P. Multistain segmentation of renal histology: first steps toward artificial intelligence-augmented digital nephropathology. Kidney Int 2021; 99:17-19. [PMID: 33390226 DOI: 10.1016/j.kint.2020.08.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 08/19/2020] [Indexed: 11/28/2022]
Abstract
Artificial intelligence (AI), and particularly deep learning (DL), are showing great potential in improving pathology diagnostics in many aspects, 1 of which is the segmentation of histology into (diagnostically) relevant compartments. Although most current studies focus on AI and DL in oncologic pathology, an increasing number of studies explore their application to nephropathology, including the study published in this issue of Kidney International by Jayapandian et al.
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Affiliation(s)
- Roman D Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Jesper Kers
- Department of Pathology, Amsterdam UMC, Amsterdam Infection & Immunity Institute, University of Amsterdam, Amsterdam, The Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands; Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany.
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14
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Bouteldja N, Klinkhammer BM, Bülow RD, Droste P, Otten SW, Freifrau von Stillfried S, Moellmann J, Sheehan SM, Korstanje R, Menzel S, Bankhead P, Mietsch M, Drummer C, Lehrke M, Kramann R, Floege J, Boor P, Merhof D. Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology. J Am Soc Nephrol 2021; 32:52-68. [PMID: 33154175 PMCID: PMC7894663 DOI: 10.1681/asn.2020050597] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 09/09/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation. METHODS We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman's capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total. RESULTS Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies. CONCLUSIONS We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies.
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Affiliation(s)
- Nassim Bouteldja
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Barbara M. Klinkhammer
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany,Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Roman D. Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Patrick Droste
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Simon W. Otten
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | | | - Julia Moellmann
- Department of Cardiology and Vascular Medicine, RWTH Aachen University Hospital, Aachen, Germany
| | | | | | - Sylvia Menzel
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Peter Bankhead
- Edinburgh Pathology, University of Edinburgh, Edinburgh, United Kingdom,Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthias Mietsch
- Laboratory Animal Science Unit, German Primate Center, Goettingen, Germany
| | - Charis Drummer
- Platform Degenerative Diseases, German Primate Center, Goettingen, Germany
| | - Michael Lehrke
- Department of Cardiology and Vascular Medicine, RWTH Aachen University Hospital, Aachen, Germany
| | - Rafael Kramann
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany,Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jürgen Floege
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany,Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
| | - Dorit Merhof
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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15
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Boss K, Woywodt A, Kribben A, Mülling N, Becker S. Digitale Nephrologie. DER NEPHROLOGE 2021; 16:57-61. [PMID: 33425034 PMCID: PMC7784214 DOI: 10.1007/s11560-020-00478-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/08/2020] [Indexed: 10/31/2022]
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16
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Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, Hodgin JB, Zee J, Hewitt SM, O'Toole J, Toro P, Sedor JR, Barisoni L, Madabhushi A. Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int 2020; 99:86-101. [PMID: 32835732 PMCID: PMC8414393 DOI: 10.1016/j.kint.2020.07.044] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 06/29/2020] [Accepted: 07/24/2020] [Indexed: 12/21/2022]
Abstract
The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman’s capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman’s capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.
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Affiliation(s)
- Catherine P Jayapandian
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
| | - Yijiang Chen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Andrew R Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; Precision Oncology Center, Lausanne University Hospital, Vaud, Switzerland
| | - Matthew B Palmer
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Miroslav Sekulic
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; Department of Pathology, University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jarcy Zee
- Department of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stephen M Hewitt
- Laboratory of Pathology, National Institutes of Health, National Cancer Institute, Bethesda, Maryland, USA
| | - John O'Toole
- Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, Ohio, USA
| | - Paula Toro
- Department of Pathology, Universidad Nacional de Colombia, Bogotá, Colombia
| | - John R Sedor
- Lerner Research and Glickman Urology and Kidney Institutes, Cleveland Clinic, Cleveland, Ohio, USA; Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Laura Barisoni
- Department of Pathology and Medicine, Division of Nephrology, Duke University, Durham, North Carolina, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA
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