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Augulis R, Rasmusson A, Laurinaviciene A, Jen KY, Laurinavicius A. Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies. Sci Rep 2024; 14:5345. [PMID: 38438513 PMCID: PMC10912734 DOI: 10.1038/s41598-024-55936-3] [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: 09/13/2023] [Accepted: 02/29/2024] [Indexed: 03/06/2024] Open
Abstract
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.
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Affiliation(s)
- Renaldas Augulis
- Department of Pathology and Forensic Medicine, Institute of Biomedical Sciences of the Faculty of Medicine, Vilnius University, M. K. Ciurlionio Str. 21, 03101, Vilnius, Lithuania.
- National Centre of Pathology, Vilnius University Hospital Santaros Klinikos, P. Baublio Str. 5, 08406, Vilnius, Lithuania.
| | - Allan Rasmusson
- Department of Pathology and Forensic Medicine, Institute of Biomedical Sciences of the Faculty of Medicine, Vilnius University, M. K. Ciurlionio Str. 21, 03101, Vilnius, Lithuania
- National Centre of Pathology, Vilnius University Hospital Santaros Klinikos, P. Baublio Str. 5, 08406, Vilnius, Lithuania
| | - Aida Laurinaviciene
- Department of Pathology and Forensic Medicine, Institute of Biomedical Sciences of the Faculty of Medicine, Vilnius University, M. K. Ciurlionio Str. 21, 03101, Vilnius, Lithuania
- National Centre of Pathology, Vilnius University Hospital Santaros Klinikos, P. Baublio Str. 5, 08406, Vilnius, Lithuania
| | - Kuang-Yu Jen
- Department of Pathology and Laboratory Medicine, University of California, Davis School of Medicine, Sacramento, CA, USA
| | - Arvydas Laurinavicius
- Department of Pathology and Forensic Medicine, Institute of Biomedical Sciences of the Faculty of Medicine, Vilnius University, M. K. Ciurlionio Str. 21, 03101, Vilnius, Lithuania
- National Centre of Pathology, Vilnius University Hospital Santaros Klinikos, P. Baublio Str. 5, 08406, Vilnius, Lithuania
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2
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Hermsen M, Ciompi F, Adefidipe A, Denic A, Dendooven A, Smith BH, van Midden D, Bräsen JH, Kers J, Stegall MD, Bándi P, Nguyen T, Swiderska-Chadaj Z, Smeets B, Hilbrands LB, van der Laak JAWM. Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:1418-1432. [PMID: 35843265 DOI: 10.1016/j.ajpath.2022.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 06/13/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
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.
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Affiliation(s)
- Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Adeyemi Adefidipe
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Amélie Dendooven
- Department of Pathology, Ghent University Hospital, Ghent, Belgium; Faculty of Medicine, University of Antwerp, Wilrijk, Antwerp, Belgium
| | - Byron H Smith
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Dominique van Midden
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jan Hinrich Bräsen
- Nephropathology Unit, Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Jesper Kers
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands; Center for Analytical Sciences Amsterdam, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Mark D Stegall
- Division of Transplantation Surgery, Mayo Clinic, Rochester, Minnesota
| | - Péter Bándi
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tri Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Zaneta Swiderska-Chadaj
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands; Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
| | - Bart Smeets
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Luuk B Hilbrands
- Department of Nephrology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen A W M van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
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Yao T, Lu Y, Long J, Jha A, Zhu Z, Asad Z, Yang H, Fogo AB, Huo Y. Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining. J Med Imaging (Bellingham) 2022; 9:052408. [PMID: 35747553 DOI: 10.1117/1.jmi.9.5.052408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. Approach: The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified-GSS (associated with hypertension-related injury), disappearing-GSS (a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent-GSS (nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. Results: The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice dimilarity coefficient. Conclusion: We develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is user-friendly to non-technical users via a single line of command. The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/Glo-In-One.
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Affiliation(s)
- Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Yuzhe Lu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Jun Long
- Central South University, Big Data Institute, Changsha, China
| | - Aadarsh Jha
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Zheyu Zhu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Zuhayr Asad
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Haichun Yang
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, Tennessee, United States
| | - Agnes B Fogo
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
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A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology. COMMUNICATIONS MEDICINE 2022; 2:105. [PMID: 35996627 PMCID: PMC9391340 DOI: 10.1038/s43856-022-00138-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 06/09/2022] [Indexed: 01/21/2023] Open
Abstract
Background Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.
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Santo BA, Govind D, Daneshpajouhnejad P, Yang X, Wang XX, Myakala K, Jones BA, Levi M, Kopp JB, Yoshida T, Niedernhofer LJ, Manthey D, Moon KC, Han SS, Zee J, Rosenberg AZ, Sarder P. PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool. Kidney Int Rep 2022; 7:1377-1392. [PMID: 35694561 PMCID: PMC9174049 DOI: 10.1016/j.ekir.2022.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/25/2022] [Accepted: 03/04/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Podocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set. Methods Whole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid–Schiff counterstain were acquired. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool. Results PodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome. Conclusion PodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.
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Affiliation(s)
- Briana A. Santo
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Darshana Govind
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA
| | | | - Xiaoping Yang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xiaoxin X. Wang
- Department of Biochemistry, Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| | - Komuraiah Myakala
- Department of Biochemistry, Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| | - Bryce A. Jones
- Department of Pharmacology and Physiology, Georgetown University, Washington, District of Columbia, USA
| | - Moshe Levi
- Department of Biochemistry, Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
| | - Jeffrey B. Kopp
- Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Teruhiko Yoshida
- Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura J. Niedernhofer
- Institute on the Biology of Aging and Metabolism, Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Kyung Chul Moon
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jarcy Zee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Avi Z. Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Correspondence: Avi Z. Rosenberg, Department of Pathology, Johns Hopkins University School of Medicine, 720 Rutland Avenue, Ross Building, Room 632D, Johns Hopkins Medical Institutions, Baltimore, Maryland 21205, USA.
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA
- Pinaki Sarder, Department of Pathology and Anatomical Sciences, University at Buffalo, 955 Main Street, Room 4204, Buffalo, New York 14203, USA.
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6
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Marechal E, Jaugey A, Tarris G, Paindavoine M, Seibel J, Martin L, Funes de la Vega M, Crepin T, Ducloux D, Zanetta G, Felix S, Bonnot PH, Bardet F, Cormier L, Rebibou JM, Legendre M. Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples. Clin J Am Soc Nephrol 2022; 17:260-270. [PMID: 34862241 PMCID: PMC8823945 DOI: 10.2215/cjn.07830621] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 11/16/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND AND OBJECTIVES The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histologic prognostic features. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS In total, 241 samples of healthy kidney tissue were split into three independent cohorts. The "Training" cohort (n=65) was used to train two convolutional neural networks: one to detect the cortex and a second to segment the kidney structures. The "Test" cohort (n=50) assessed their performance by comparing manually outlined regions of interest to predicted ones. The "Application" cohort (n=126) compared prognostic histologic data obtained manually or through the algorithm on the basis of the combination of the two convolutional neural networks. RESULTS In the Test cohort, the networks isolated the cortex and segmented the elements of interest with good performances (>90% of the cortex, healthy tubules, glomeruli, and even globally sclerotic glomeruli were detected). In the Application cohort, the expected and predicted prognostic data were significantly correlated. The correlation coefficients r were 0.85 for glomerular volume, 0.51 for glomerular density, 0.75 for interstitial fibrosis, 0.71 for tubular atrophy, and 0.73 for vascular intimal thickness, respectively. The algorithm had a good ability to predict significant (>25%) tubular atrophy and interstitial fibrosis level (receiver operator characteristic curve with an area under the curve, 0.92 and 0.91, respectively) or a significant vascular luminal stenosis (>50%) (area under the curve, 0.85). CONCLUSION This freely available tool enables the automated segmentation of kidney tissue to obtain prognostic histologic data in a fast, objective, reliable, and reproducible way.
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Affiliation(s)
- Elise Marechal
- Department of Nephrology, CHU Dijon, France,Université de Bourgogne Franche comté, France,UMR 1098, INCREASE, Besançon, France
| | - Adrien Jaugey
- Université de Bourgogne Franche comté, France,ESIREM school, Dijon, France
| | - Georges Tarris
- Université de Bourgogne Franche comté, France,Department of Pathology, CHU Besançon France
| | - Michel Paindavoine
- Université de Bourgogne Franche comté, France,ESIREM school, Dijon, France,Laboratoire de l’étude de l’apprentissage et du Développement, Dijon, France
| | - Jean Seibel
- Department of Nephrology, CHU Dijon, France,Department of Nephrology, CHU Besançon, France
| | - Laurent Martin
- Université de Bourgogne Franche comté, France,Department of Pathology, CHU Dijon, France
| | | | - Thomas Crepin
- Université de Bourgogne Franche comté, France,UMR 1098, INCREASE, Besançon, France,Department of Nephrology, CHU Besançon, France
| | - Didier Ducloux
- Université de Bourgogne Franche comté, France,UMR 1098, INCREASE, Besançon, France,Department of Nephrology, CHU Besançon, France
| | | | | | | | - Florian Bardet
- Université de Bourgogne Franche comté, France,Department of Urology, CHU Dijon France
| | - Luc Cormier
- Université de Bourgogne Franche comté, France,Department of Urology, CHU Dijon France
| | - Jean-Michel Rebibou
- Department of Nephrology, CHU Dijon, France,Université de Bourgogne Franche comté, France,UMR 1098, INCREASE, Besançon, France
| | - Mathieu Legendre
- Department of Nephrology, CHU Dijon, France,Université de Bourgogne Franche comté, France,UMR 1098, INCREASE, Besançon, France
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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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Zheng Y, Cassol CA, Jung S, Veerapaneni D, Chitalia VC, Ren KYM, Bellur SS, Boor P, Barisoni LM, Waikar SS, Betke M, Kolachalama VB. Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1442-1453. [PMID: 34033750 PMCID: PMC8453248 DOI: 10.1016/j.ajpath.2021.05.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/01/2021] [Accepted: 05/11/2021] [Indexed: 12/25/2022]
Abstract
Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.
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Affiliation(s)
- Yi Zheng
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; Department of Computer Science, College of Arts and Sciences, Boston University, Boston, Massachusetts
| | - Clarissa A Cassol
- Arkana Laboratories, Little Rock, Arkansas; Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Saemi Jung
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Divya Veerapaneni
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - Vipul C Chitalia
- Section of Nephrology, Boston University School of Medicine & Boston Medical Center, Boston, Massachusetts
| | - Kevin Y M Ren
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada
| | - Shubha S Bellur
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada; Medical Renal and Genitourinary Pathology, William Osler Health System, Brampton, Ontario, Canada
| | - Peter Boor
- Institute of Pathology & Department of Nephrology & Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany
| | - Laura M Barisoni
- Department of Pathology and Medicine, Duke University, Durham, North Carolina
| | - Sushrut S Waikar
- Section of Nephrology, Boston University School of Medicine & Boston Medical Center, Boston, Massachusetts
| | - Margrit Betke
- Department of Computer Science, College of Arts and Sciences, Boston University, Boston, Massachusetts; Faculty of Computing and Data Sciences, Boston University, Boston, Massachusetts
| | - Vijaya B Kolachalama
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts; Department of Computer Science, College of Arts and Sciences, Boston University, Boston, Massachusetts; Faculty of Computing and Data Sciences, Boston University, Boston, Massachusetts.
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9
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Huo Y, Deng R, Liu Q, Fogo AB, Yang H. AI applications in renal pathology. Kidney Int 2021; 99:1309-1320. [PMID: 33581198 PMCID: PMC8154730 DOI: 10.1016/j.kint.2021.01.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 12/20/2022]
Abstract
The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.
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Affiliation(s)
- Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Ruining Deng
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Quan Liu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Haichun Yang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
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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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Lutnick B, Kammardi Shashiprakash A, Manthey D, Sarder P. User friendly, cloud based, whole slide image segmentation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11603. [PMID: 34366542 DOI: 10.1117/12.2581383] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. We have developed a plugin for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. This plugin runs a state-of-the-art convolutional neural network for segmentation of WSIs in the cloud. Our plugin is built on the open source tool HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data management and viewing abilities for WSI datasets. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Users can easily upload slides to a server where our plugin is installed and perform the segmentation analysis remotely. This plugin is open source and once trained, has the ability to be applied to the segmentation of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, demonstrating it on holdout tissue slides.
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Affiliation(s)
- Brendon Lutnick
- Department of Pathology and Anatomical Sciences, SUNY Buffalo
| | | | | | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, SUNY Buffalo
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12
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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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13
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Hermsen M, Smeets B, Hilbrands L, van der Laak J. Artificial intelligence: is there a potential role in nephropathology? Nephrol Dial Transplant 2020; 37:438-440. [PMID: 32995871 PMCID: PMC8875471 DOI: 10.1093/ndt/gfaa181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Indexed: 11/14/2022] Open
Affiliation(s)
- Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bart Smeets
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luuk Hilbrands
- Department of Nephrology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
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Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:jcm9041107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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