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Fan F, Liu Q, Zee J, Ozeki T, Demeke D, Yang Y, Farris AB, Wang B, Shah M, Jacobs J, Mariani L, Lafata K, Rubin J, Chen Y, Holzman L, Hodgin JB, Madabhushi A, Barisoni L, Janowczyk A. Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.19.24310619. [PMID: 39072032 PMCID: PMC11275675 DOI: 10.1101/2024.07.19.24310619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Background Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Methods Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. Results N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA. Conclusions Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.
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Affiliation(s)
- Fan Fan
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Qian Liu
- Children's Hospital of Philadelphia Research Institute, Philadelphia, PA
| | - Jarcy Zee
- Children's Hospital of Philadelphia Research Institute, Philadelphia, PA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Takaya Ozeki
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Dawit Demeke
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Yingbao Yang
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Alton B Farris
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Bangcheng Wang
- Department of Pathology, Division of AI & Computational Pathology, Duke University, Durham, NC, United States
| | - Manav Shah
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Jackson Jacobs
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Laura Mariani
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Kyle Lafata
- Department of Radiation Oncology, Duke University, Durham, NC, United States
| | - Jeremy Rubin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Yijiang Chen
- Department of Radiation Oncology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Lawrence Holzman
- Department of Medicine, Division of Nephrology and Hypertension, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeffrey B Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA
| | - Laura Barisoni
- Department of Pathology, Division of AI & Computational Pathology, Duke University, Durham, NC, United States
- Department of Medicine, Division of Nephrology, Duke University, Durham, NC, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland
- Department of Diagnostics, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
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Ye Y, Xia L, Yang S, Luo Y, Tang Z, Li Y, Han L, Xie H, Ren Y, Na N. Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images. Front Immunol 2024; 15:1438247. [PMID: 39034991 PMCID: PMC11257957 DOI: 10.3389/fimmu.2024.1438247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Accepted: 06/21/2024] [Indexed: 07/23/2024] Open
Abstract
Background Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection. Method We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations. Results In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively. Conclusion We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.
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Affiliation(s)
- Yongrong Ye
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liubing Xia
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shicong Yang
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - You Luo
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zuofu Tang
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
- Research Center for Brain-Computer Interface, Pazhou Lab, Guangzhou, China
| | - Lanqing Han
- Center for Artificial Intelligence in Medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China
| | - Hanbin Xie
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yong Ren
- Scientific Research Project Department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, Guangzhou, China
- Shensi lab, Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen, China
- The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Ning Na
- Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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3
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Bülow RD, Lan YC, Amann K, Boor P. [Artificial intelligence in kidney transplant pathology]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:277-283. [PMID: 38598097 DOI: 10.1007/s00292-024-01324-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/12/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology. AIM Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook. MATERIALS AND METHODS Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics. RESULTS AND CONCLUSION Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.
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Affiliation(s)
- Roman David Bülow
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland
| | - Yu-Chia Lan
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland
| | - Kerstin Amann
- Abteilung Nephropathologie, Institut für Pathologie, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Deutschland
| | - Peter Boor
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland.
- Medizinische Klinik II, Universitätsklinikum RWTH Aachen, Aachen, Deutschland.
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Hirai T, Kondo A, Shimizu T, Fukuda H, Tokita D, Takagi T, Mayer AT, Ishida H. Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging. Transplantation 2024:00007890-990000000-00800. [PMID: 38913785 DOI: 10.1097/tp.0000000000005107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
BACKGROUND Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. METHODS The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. RESULTS In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. CONCLUSIONS Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.
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Affiliation(s)
- Toshihito Hirai
- Department of Urology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | | | - Tomokazu Shimizu
- Division of Organ Transplant Management, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Hironori Fukuda
- Department of Urology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | - Daisuke Tokita
- Department of Urology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
- Center for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku, Tokyo, Japan
| | - Toshio Takagi
- Department of Urology, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
| | | | - Hideki Ishida
- Division of Organ Transplant Management, Tokyo Women's Medical University, Shinjuku, Tokyo, Japan
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Barsotti GC, Luciano R, Kumar A, Meliambro K, Kakade V, Tokita J, Naik A, Fu J, Peck E, Pell J, Reghuvaran A, Tanvir E, Patel P, Zhang W, Li F, Moeckel G, Perincheri S, Cantley L, Moledina DG, Wilson FP, He JC, Menon MC. Rationale and Design of a Phase 2, Double-blind, Placebo-Controlled, Randomized Trial Evaluating AMP Kinase-Activation by Metformin in Focal Segmental Glomerulosclerosis. Kidney Int Rep 2024; 9:1354-1368. [PMID: 38707807 PMCID: PMC11068976 DOI: 10.1016/j.ekir.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction Focal segmental glomerulosclerosis (FSGS), the most common primary glomerular disease leading to end-stage kidney disease (ESKD), is characterized by podocyte injury and depletion, whereas minimal change disease (MCD) has better outcomes despite podocyte injury. Identifying mechanisms capable of preventing podocytopenia during injury could transform FSGS to an "MCD-like" state. Preclinical data have reported conversion of an MCD-like injury to one with podocytopenia and FSGS by inhibition of AMP-kinase (AMPK) in podocytes. Conversely, in FSGS, AMPK-activation using metformin (MF) mitigated podocytopenia and azotemia. Observational studies also support beneficial effects of MF on proteinuria and chronic kidney disease (CKD) outcomes in diabetes. A randomized controlled trial (RCT) to test MF in podocyte injury with FSGS has not yet been conducted. Methods We report the rationale and design of phase 2, double-blind, placebo-controlled RCT evaluating the efficacy and safety of MF as adjunctive therapy in FSGS. By randomizing 30 patients with biopsy-confirmed FSGS to MF or placebo (along with standard immunosuppression), we will study mechanistic biomarkers that correlate with podocyte injury or depletion and evaluate outcomes after 6 months. We specifically integrate novel urine, blood, and tissue markers as surrogates for FSGS progression along with unbiased profiling strategies. Results and Conclusion Our phase 2 trial will provide insight into the potential efficacy and safety of MF as adjunctive therapy in FSGS-a crucial step to developing a larger phase 3 study. The mechanistic assays here will guide the design of other FSGS trials and contribute to understanding AMPK activation as a potential therapeutic target in FSGS. By repurposing an inexpensive agent, our results will have implications for FSGS treatment in resource-poor settings.
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Affiliation(s)
- Gabriel C. Barsotti
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Randy Luciano
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Ashwani Kumar
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Kristin Meliambro
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Vijayakumar Kakade
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Joji Tokita
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Abhijit Naik
- Division of Nephrology, Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
| | - Jia Fu
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Elizabeth Peck
- Clinical Research Coordinator, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John Pell
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Anand Reghuvaran
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - E.M. Tanvir
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Prashant Patel
- Investigational Drug Service, Department of Pharmacy Services, Yale New Haven Hospital, Connecticut, USA
| | - Weijia Zhang
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Gilbert Moeckel
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sudhir Perincheri
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lloyd Cantley
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Dennis G. Moledina
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - F. Perry Wilson
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - John C. He
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Madhav C. Menon
- Section of Nephrology, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
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Tam KH, Soares MF, Kers J, Sharples EJ, Ploeg RJ, Kaisar M, Rittscher J. Predicting clinical endpoints and visual changes with quality-weighted tissue-based renal histological features. FRONTIERS IN TRANSPLANTATION 2024; 3:1305468. [PMID: 38993786 PMCID: PMC11235227 DOI: 10.3389/frtra.2024.1305468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 03/15/2024] [Indexed: 07/13/2024]
Abstract
Two common obstacles limiting the performance of data-driven algorithms in digital histopathology classification tasks are the lack of expert annotations and the narrow diversity of datasets. Multi-instance learning (MIL) can address the former challenge for the analysis of whole slide images (WSI), but performance is often inferior to full supervision. We show that the inclusion of weak annotations can significantly enhance the effectiveness of MIL while keeping the approach scalable. An analysis framework was developed to process periodic acid-Schiff (PAS) and Sirius Red (SR) slides of renal biopsies. The workflow segments tissues into coarse tissue classes. Handcrafted and deep features were extracted from these tissues and combined using a soft attention model to predict several slide-level labels: delayed graft function (DGF), acute tubular injury (ATI), and Remuzzi grade components. A tissue segmentation quality metric was also developed to reduce the adverse impact of poorly segmented instances. The soft attention model was trained using 5-fold cross-validation on a mixed dataset and tested on the QUOD dataset containing n = 373 PAS and n = 195 SR biopsies. The average ROC-AUC over different prediction tasks was found to be 0.598 ± 0.011 , significantly higher than using only ResNet50 ( 0.545 ± 0.012 ), only handcrafted features ( 0.542 ± 0.011 ), and the baseline ( 0.532 ± 0.012 ) of state-of-the-art performance. In conjunction with soft attention, weighting tissues by segmentation quality has led to further improvement ( A U C = 0.618 ± 0.010 ) . Using an intuitive visualisation scheme, we show that our approach may also be used to support clinical decision making as it allows pinpointing individual tissues relevant to the predictions.
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Affiliation(s)
- Ka Ho Tam
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Maria F. Soares
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - 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
| | - Edward J. Sharples
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Rutger J. Ploeg
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
- Research and Development, NHS Blood and Transplant Filton and Oxford, Oxford, United Kingdom
| | - Maria Kaisar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
- Research and Development, NHS Blood and Transplant Filton and Oxford, Oxford, United Kingdom
| | - Jens Rittscher
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
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7
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Al Moussawy M, Lakkis ZS, Ansari ZA, Cherukuri AR, Abou-Daya KI. The transformative potential of artificial intelligence in solid organ transplantation. FRONTIERS IN TRANSPLANTATION 2024; 3:1361491. [PMID: 38993779 PMCID: PMC11235281 DOI: 10.3389/frtra.2024.1361491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/01/2024] [Indexed: 07/13/2024]
Abstract
Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.
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Affiliation(s)
- Mouhamad Al Moussawy
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zoe S Lakkis
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zuhayr A Ansari
- Health Sciences Research Training Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Aravind R Cherukuri
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
| | - Khodor I Abou-Daya
- Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States
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8
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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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9
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Roufosse C, Naesens M, Haas M, Lefaucheur C, Mannon RB, Afrouzian M, Alachkar N, Aubert O, Bagnasco SM, Batal I, Bellamy COC, Broecker V, Budde K, Clahsen-Van Groningen M, Coley SM, Cornell LD, Dadhania D, Demetris AJ, Einecke G, Farris AB, Fogo AB, Friedewald J, Gibson IW, Horsfield C, Huang E, Husain SA, Jackson AM, Kers J, Kikić Ž, Klein A, Kozakowski N, Liapis H, Mangiola M, Montgomery RA, Nankinvell B, Neil DAH, Nickerson P, Rabant M, Randhawa P, Riella LV, Rosales I, Royal V, Sapir-Pichhadze R, Sarder P, Sarwal M, Schinstock C, Stegall M, Solez K, van der Laak J, Wiebe C, Colvin RB, Loupy A, Mengel M. The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts. Am J Transplant 2024; 24:350-361. [PMID: 37931753 PMCID: PMC11135910 DOI: 10.1016/j.ajt.2023.10.031] [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: 09/01/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023]
Abstract
The XVIth Banff Meeting for Allograft Pathology was held in Banff, Alberta, Canada, from September 19 to 23, 2022, as a joint meeting with the Canadian Society of Transplantation. In addition to a key focus on the impact of microvascular inflammation and biopsy-based transcript analysis on the Banff Classification, further sessions were devoted to other aspects of kidney transplant pathology, in particular T cell-mediated rejection, activity and chronicity indices, digital pathology, xenotransplantation, clinical trials, and surrogate endpoints. Although the output of these sessions has not led to any changes in the classification, the key role of Banff Working Groups in phrasing unanswered questions, and coordinating and disseminating results of investigations addressing these unanswered questions was emphasized. This paper summarizes the key Banff Meeting 2022 sessions not covered in the Banff Kidney Meeting 2022 Report paper and also provides an update on other Banff Working Group activities relevant to kidney allografts.
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Affiliation(s)
- Candice Roufosse
- Department of Immunology and Inflammation, Faculty Medicine, Imperial College London, London, UK.
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
| | - Mark Haas
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Carmen Lefaucheur
- Université Paris Cité, INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, France & Department of Nephrology and Transplantation, Saint-Louis Hospital, Paris, France
| | - Roslyn B Mannon
- Department of Internal Medicine, Division of Nephrology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Marjan Afrouzian
- Department of Pathology, University of Texas Medical Branch at Galveston, Texas, USA
| | - Nada Alachkar
- Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Olivier Aubert
- Université Paris Cité, INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, France & Department of Transplantation, Necker Hospital, Paris, France
| | - Serena M Bagnasco
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Ibrahim Batal
- Pathology & Cell Biology, Columbia University Irving Medical Center, New York, USA
| | | | - Verena Broecker
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Klemens Budde
- Department of Nephrology, Charité Universitätsmedizin, Berlin, Germany
| | - Marian Clahsen-Van Groningen
- Department of Pathology and Clinical Bioinformatics, Erasmus University Center Rotterdam, Rotterdam, Netherlands; Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Shana M Coley
- Transplant Translational Research, Arkana Laboratories, Arkansas, USA
| | - Lynn D Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Darshana Dadhania
- Department Medicine, Weill Cornell Medical College of Cornell University, New York, New York, USA
| | - Anthony J Demetris
- UPMC Hepatic and Transplantation Pathology, Pittsburg, Pennsylvania, USA
| | - Gunilla Einecke
- Department of Nephrology and Rheumatology, University Medical Center Göttingen, Germany
| | - Alton B Farris
- Department of Pathology and Laboratory Medicine, Emory University, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John Friedewald
- Comprehensive Transplant Center, Northwestern University, USA
| | - Ian W Gibson
- Department of Pathology, University of Manitoba, Winnipeg, Canada
| | | | - Edmund Huang
- Department of Medicine, Division of Nephrology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Syed A Husain
- Division of Nephrology, Columbia University, New York, New York, USA
| | | | - Jesper Kers
- Department of Pathology, Leiden University Medical Center, Netherlands; Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Željko Kikić
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | | | | | - Helen Liapis
- Ludwig Maximillian University Munich, Nephrology Center, Germany
| | | | | | - Brian Nankinvell
- Department of Renal Medicine, Westmead Hospital, Westmead, New South Wales, Australia
| | - Desley A H Neil
- Department of Cellular Pathology, Queen Elizabeth Hospital Birmingham and Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Peter Nickerson
- Department of Medicine and Department of Immunology, University of Manitoba, Winnipeg, Canada
| | - Marion Rabant
- Pathology department, Necker-Enfants Malades Hospital, Paris, France
| | - Parmjeet Randhawa
- Pathology, Thomas E. Starzl Transplant Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Leonardo V Riella
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ivy Rosales
- Immunopathology Research Laboratory, Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Virginie Royal
- Maisonneuve-Rosemont Hospital, University of Montreal, Quebec, Canada
| | - Ruth Sapir-Pichhadze
- Division of Nephrology & Multiorgan Transplant Program, McGill University, Montreal, Quebec, Canada
| | - Pinaki Sarder
- Department of Medicine-Quantitative Health, University of Florida College of Medicine, Florida, USA
| | - Minnie Sarwal
- Division of MultiOrgan Transplantation, UCSF, San Francisco, California, USA
| | - Carrie Schinstock
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark Stegall
- Department Transplantation Surgery, Mayo Clinic, Rochester, Massachusetts, USA
| | - Kim Solez
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | | | - Chris Wiebe
- Department of Medicine and Department of Immunology, University of Manitoba, Winnipeg, Canada
| | - Robert B Colvin
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandre Loupy
- Université Paris Cité, INSERM, PARCC, Paris Institute for Transplantation and Organ Regeneration, France & Department of Transplantation, Necker Hospital, Paris, France
| | - Michael Mengel
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
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10
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Yi Z, Xi C, Menon MC, Cravedi P, Tedla F, Soto A, Sun Z, Liu K, Zhang J, Wei C, Chen M, Wang W, Veremis B, Garcia-Barros M, Kumar A, Haakinson D, Brody R, Azeloglu EU, Gallon L, O'Connell P, Naesens M, Shapiro R, Colvin RB, Ward S, Salem F, Zhang W. A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization. Kidney Int 2024; 105:281-292. [PMID: 37923131 PMCID: PMC10892475 DOI: 10.1016/j.kint.2023.09.031] [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: 01/02/2023] [Revised: 09/07/2023] [Accepted: 09/25/2023] [Indexed: 11/07/2023]
Abstract
Lesion scores on procurement donor biopsies are commonly used to guide organ utilization for deceased-donor kidneys. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropriate discard. Therefore, we constructed deep-learning based models to recognize kidney tissue compartments in hematoxylin & eosin-stained sections from procurement needle biopsies performed nationwide in years 2011-2020. To do this, we extracted whole-slide abnormality features from 2431 kidneys and correlated with pathologists' scores and transplant outcomes. A Kidney Donor Quality Score (KDQS) was derived and used in combination with recipient demographic and peri-transplant characteristics to predict graft loss or assist organ utilization. The performance on wedge biopsies was additionally evaluated. Our model identified 96% and 91% of normal/sclerotic glomeruli respectively; 94% of arteries/arterial intimal fibrosis; 90% of tubules. Whole-slide features of Sclerotic Glomeruli (GS)%, Arterial Intimal Fibrosis (AIF)%, and Interstitial Space Abnormality (ISA)% demonstrated strong correlations with corresponding pathologists' scores of all 2431 kidneys, but had superior associations with post-transplant estimated glomerular filtration rates in 2033 and graft loss in 1560 kidneys. The combination of KDQS and other factors predicted one- and four-year graft loss in a discovery set of 520 kidneys and a validation set of 1040 kidneys. By using the composite KDQS of 398 discarded kidneys due to "biopsy findings", we suggest that if transplanted, 110 discarded kidneys could have had similar survival to that of other transplanted kidneys. Thus, our composite KDQS and survival prediction models may facilitate risk stratification and organ utilization while potentially reducing unnecessary organ discard.
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Affiliation(s)
- Zhengzi Yi
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Caixia Xi
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Madhav C Menon
- Nephrology Division, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Paolo Cravedi
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Fasika Tedla
- The Recanati/Miller Transplantation Institute (RMTI), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alan Soto
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zeguo Sun
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Keyu Liu
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Jason Zhang
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Chengguo Wei
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Man Chen
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Wenlin Wang
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Brandon Veremis
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Monica Garcia-Barros
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Abhishek Kumar
- Nephrology Division, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Danielle Haakinson
- Nephrology Division, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Rachel Brody
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Evren U Azeloglu
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA
| | - Lorenzo Gallon
- Northwestern Medicine Organ Transplantation Center, Northwestern University, Chicago, Illinois, USA
| | - Philip O'Connell
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Maarten Naesens
- Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium; Department of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium
| | - Ron Shapiro
- The Recanati/Miller Transplantation Institute (RMTI), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert B Colvin
- Department of Pathology, Massachusetts General Hospital. Boston, Massachusetts, USA; Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephen Ward
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Fadi Salem
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
| | - Weijia Zhang
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York New York, USA.
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11
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Feng C, Ong K, Young DM, Chen B, Li L, Huo X, Lu H, Gu W, Liu F, Tang H, Zhao M, Yang M, Zhu K, Huang L, Wang Q, Marini GPL, Gui K, Han H, Sanders SJ, Li L, Yu W, Mao J. Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis. Bioinformatics 2024; 40:btad740. [PMID: 38058211 PMCID: PMC10796177 DOI: 10.1093/bioinformatics/btad740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 11/13/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023] Open
Abstract
MOTIVATION Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. AVAILABILITY AND IMPLEMENTATION https://github.com/ChunyueFeng/Kidney-DataSet.
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Affiliation(s)
- Chunyue Feng
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
| | - Kokhaur Ong
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
| | - David M Young
- Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94143, United States
| | - Bingxian Chen
- Ningbo Konfoong Bioinformation Tech Co., Ltd., Ningbo 315000, China
| | - Longjie Li
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
| | - Xinmi Huo
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
| | - Haoda Lu
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
- Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Weizhong Gu
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Fei Liu
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
| | - Hongfeng Tang
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Manli Zhao
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Min Yang
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Kun Zhu
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Limin Huang
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
| | - Qiang Wang
- Ningbo Konfoong Bioinformation Tech Co., Ltd., Ningbo 315000, China
| | | | - Kun Gui
- Ningbo Konfoong Bioinformation Tech Co., Ltd., Ningbo 315000, China
| | - Hao Han
- Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94143, United States
| | - Lin Li
- Department of Nephrology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Weimiao Yu
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
- Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jianhua Mao
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
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12
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Cazzaniga G, Rossi M, Eccher A, Girolami I, L'Imperio V, Van Nguyen H, Becker JU, Bueno García MG, Sbaraglia M, Dei Tos AP, Gambaro G, Pagni F. Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions. J Nephrol 2024; 37:65-76. [PMID: 37768550 PMCID: PMC10920416 DOI: 10.1007/s40620-023-01775-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. METHODS Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on non-neoplastic kidney histological samples were included and analyzed to extract information such as publication year, AI task, and learning type. Prepublication servers and reviews were not included. RESULTS Seventy-six (76) original research articles were selected. Most of the studies were conducted in the United States in the last 7 years. To date, research has been mainly conducted on relatively easy tasks, like single-stain glomerular segmentation. However, there is a trend towards developing more complex tasks such as glomerular multi-stain classification. CONCLUSION Deep learning has been used to identify patterns in complex histopathology data and looks promising for the comprehensive assessment of renal biopsy, through the use of multiple stains and virtual staining techniques. Hybrid and collaborative learning approaches have also been explored to utilize large amounts of unlabeled data. A diverse team of experts, including nephropathologists, computer scientists, and clinicians, is crucial for the development of AI systems for nephropathology. Collaborative efforts among multidisciplinary experts result in clinically relevant and effective AI tools.
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Affiliation(s)
- Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy.
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, Piazzale Aristide Stefani, 1, 37126, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Ilaria Girolami
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy
| | - Hien Van Nguyen
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77004, USA
| | - Jan Ulrich Becker
- Institute of Pathology, University Hospital of Cologne, Cologne, Germany
| | - María Gloria Bueno García
- VISILAB Research Group, E.T.S. Ingenieros Industriales, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Marta Sbaraglia
- Department of Pathology, Azienda Ospedale-Università Padova, Padua, Italy
- Department of Medicine, University of Padua School of Medicine, Padua, Italy
| | - Angelo Paolo Dei Tos
- Department of Pathology, Azienda Ospedale-Università Padova, Padua, Italy
- Department of Medicine, University of Padua School of Medicine, Padua, Italy
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, Piazzale Aristide Stefani, 1, 37126, Verona, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, Università di Milano-Bicocca, Monza, Italy
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13
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Wingfield LR, Salaun A, Khan A, Webb H, Zhu T, Knight S. Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review. Transplantation 2024; 108:72-99. [PMID: 37143191 DOI: 10.1097/tp.0000000000004627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.
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Affiliation(s)
- Laura R Wingfield
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Achille Salaun
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aparajita Khan
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Helena Webb
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Simon Knight
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
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14
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Peloso A, Naesens M, Thaunat O. The Dawn of a New Era in Kidney Transplantation: Promises and Limitations of Artificial Intelligence for Precision Diagnostics. Transpl Int 2023; 36:12010. [PMID: 38234305 PMCID: PMC10793260 DOI: 10.3389/ti.2023.12010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 01/19/2024]
Affiliation(s)
- Andrea Peloso
- Division of Transplantation, Department of Surgery, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- Division of Abdominal Surgery, Department of Surgery, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Olivier Thaunat
- International Center of Infectiology Research (CIRI), French Institute of Health and Medical Research (INSERM) Unit 1111, Claude Bernard University Lyon I, National Center for Scientific Research (CNRS) Mixed University Unit (UMR) 5308, Ecole Normale Supérieure de Lyon, University of Lyon, Lyon, France
- Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Edouard Herriot Hospital, Lyon, France
- Lyon-Est Medical Faculty, Claude Bernard University (Lyon 1), Lyon, France
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15
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Jacq A, Tarris G, Jaugey A, Paindavoine M, Maréchal E, Bard P, Rebibou JM, Ansart M, Calmo D, Bamoulid J, Tinel C, Ducloux D, Crepin T, Chabannes M, Funes de la Vega M, Felix S, Martin L, Legendre M. Automated evaluation with deep learning of total interstitial inflammation and peritubular capillaritis on kidney biopsies. Nephrol Dial Transplant 2023; 38:2786-2798. [PMID: 37197910 DOI: 10.1093/ndt/gfad094] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management. METHODS We used a convolutional neural network to evaluate those criteria on kidney biopsies. A total of 423 kidney samples from various diseases were included; 83 kidney samples were used for the neural network training, 106 for comparing manual annotations on limited areas to automated predictions, and 234 to compare automated and visual gradings. RESULTS The precision, recall and F-score for leukocyte detection were, respectively, 81%, 71% and 76%. Regarding peritubular capillaries detection the precision, recall and F-score were, respectively, 82%, 83% and 82%. There was a strong correlation between the predicted and observed grading of total inflammation, as for the grading of capillaritis (r = 0.89 and r = 0.82, respectively, all P < .0001). The areas under the receiver operating characteristics curves for the prediction of pathologists' Banff total inflammation (ti) and peritubular capillaritis (ptc) scores were respectively all above 0.94 and 0.86. The kappa coefficients between the visual and the neural networks' scores were respectively 0.74, 0.78 and 0.68 for ti ≥1, ti ≥2 and ti ≥3, and 0.62, 0.64 and 0.79 for ptc ≥1, ptc ≥2 and ptc ≥3. In a subgroup of patients with immunoglobulin A nephropathy, the inflammation severity was highly correlated to kidney function at biopsy on univariate and multivariate analyses. CONCLUSION We developed a tool using deep learning that scores the total inflammation and capillaritis, demonstrating the potential of artificial intelligence in kidney pathology.
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Affiliation(s)
- Amélie Jacq
- Department of Nephrology, CHU Dijon, Dijon, France
| | | | - Adrien Jaugey
- ESIREM School, Dijon, France
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | - Michel Paindavoine
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | | | - Patrick Bard
- ESIREM School, Dijon, France
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | - Jean-Michel Rebibou
- Department of Nephrology, CHU Dijon, Dijon, France
- UMR 1098, INCREASE, Besançon, France
| | - Manon Ansart
- ESIREM School, Dijon, France
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | - Doris Calmo
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Jamal Bamoulid
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Claire Tinel
- Department of Nephrology, CHU Dijon, Dijon, France
| | - Didier Ducloux
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Thomas Crepin
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Melchior Chabannes
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | | | - Sophie Felix
- Department of Pathology, CHU Besançon, Besançon, France
| | | | - Mathieu Legendre
- Department of Nephrology, CHU Dijon, Dijon, France
- UMR 1098, INCREASE, Besançon, France
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16
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Asghar MS, Denic A, Mullan AF, Moustafa A, Barisoni L, Alexander MP, Stegall MD, Augustine J, Leibovich BC, Thompson RH, Rule AD. Age-Based Versus Young-Adult Thresholds for Nephrosclerosis on Kidney Biopsy and Prognostic Implications for CKD. J Am Soc Nephrol 2023; 34:1421-1432. [PMID: 37254246 PMCID: PMC10400104 DOI: 10.1681/asn.0000000000000171] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
SIGNIFICANCE STATEMENT Nephrosclerosis (glomerulosclerosis, interstitial fibrosis, and tubular atrophy) is the defining pathology of both kidney aging and CKD. Optimal thresholds for nephrosclerosis that identify persons with a progressive disease are unknown. This study determined a young-age threshold (18-29 years) and age-based 95th percentile thresholds for nephrosclerosis on the basis of morphometry of kidney biopsy sections from normotensive living kidney donors. These thresholds were 7.1-fold to 36-fold higher in older (70 years or older) versus younger (aged 18-29 years) normotensive donors. Age-based thresholds, but not young-age threshold, were prognostic for determining risk of progressive CKD among patients who underwent a radical nephrectomy or a for-cause native kidney biopsy, suggesting that age-based thresholds are more useful than a single young-age threshold for identifying CKD on biopsy. BACKGROUND Nephrosclerosis, defined by globally sclerotic glomeruli (GSG) and interstitial fibrosis and tubular atrophy (IFTA), is a pathology of both kidney aging and CKD. A comparison of risk of progressive CKD using aged-based thresholds for nephrosclerosis versus a single young-adult threshold is needed. METHODS We conducted morphometric analyses of kidney biopsy images for %GSG, %IFTA, and IFTA foci density among 3020 living kidney donors, 1363 patients with kidney tumor, and 314 patients with native kidney disease. Using normotensive donors, we defined young-age thresholds (18-29 years) and age-based (roughly by decade) 95th percentile thresholds. We compared age-adjusted risk of progressive CKD (kidney failure or 40% decline in eGFR) between nephrosclerosis that was "normal compared with young," "normal for age but abnormal compared with young," and "abnormal for age" in patients with tumor and patients with kidney disease. RESULTS The 95th percentiles in the youngest group (18-29 years) to the oldest group (70 years or older) ranged from 1.7% to 16% for %GSG, 0.18% to 6.5% for %IFTA, and 8.2 to 59.3 per cm 2 for IFTA foci density. Risk of progressive CKD did not differ between persons with nephrosclerosis "normal compared with young" versus "normal for age but abnormal compared with young." Risk of progressive CKD was significantly higher with %GSG, %IFTA, or IFTA foci density that was abnormal versus normal for age in both cohorts. CONCLUSIONS Given that increased risk of progressive CKD occurs only when nephrosclerosis is abnormal for age, age-based thresholds for nephrosclerosis seem to be better than a single young-age threshold for identifying clinically relevant CKD.
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Affiliation(s)
- Muhammad S. Asghar
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Aidan F. Mullan
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota
| | - Amr Moustafa
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Laura Barisoni
- Department of Pathology and Medicine, Duke University, Durham, North Carolina
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Mark D. Stegall
- Department of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
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17
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Virmani S, Rao A, Menon MC. Allograft tissue under the microscope: only the beginning. Curr Opin Organ Transplant 2023; 28:126-132. [PMID: 36787238 PMCID: PMC10214011 DOI: 10.1097/mot.0000000000001052] [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] [Indexed: 02/15/2023]
Abstract
PURPOSE OF REVIEW To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema. RECENT FINDINGS Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation. SUMMARY Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.
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Affiliation(s)
- Sarthak Virmani
- Section of Nephrology, Division of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
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18
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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: 36] [Impact Index Per Article: 36.0] [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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19
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Population Characteristics and Clinical Outcomes from the Renal Transplant Outcome Prediction Validation Study (TOPVAS). J Clin Med 2022; 11:jcm11247421. [PMID: 36556037 PMCID: PMC9781432 DOI: 10.3390/jcm11247421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Kidney transplantation is the preferred method for selected patients with kidney failure. Despite major improvements over the last decades, a significant proportion of organs are still lost every year. Causes of graft loss and impaired graft function are incompletely understood and prognostic tools are lacking. Here, we describe baseline characteristics and outcomes of the non-interventional Transplant Outcome Prediction Validation Study (TOPVAS). A total of 241 patients receiving a non-living kidney transplant were recruited in three Austrian transplantation centres and treated according to local practices. Clinical information as well as blood and urine samples were obtained at baseline and consecutive follow-ups up to 24 months. Out of the overall 16 graft losses, 11 occurred in the first year. The patient survival rate was 96.7% (95% CI: 94.3-99.1%) in the first year and 94.3% (95% CI: 91.1-97.7%) in the second year. Estimated glomerular filtration rate (eGFR) improved from 37.1 ± 14.0 mL/min/1.73 m2 at hospital discharge to 45.0 ± 14.5 mL/min/1.73 m2 at 24 months. The TOPVAS study provides information on current kidney graft and patient survival, eGFR trajectories, and rejection rates, as well as infectious and surgical complication rates under different immunosuppressive drug regimens. More importantly, it provides an extensive and well-characterized biobank for the future discovery and validation of prognostic methods.
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20
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WARS1, TYMP and GBP1 display a distinctive microcirculation pattern by immunohistochemistry during antibody-mediated rejection in kidney transplantation. Sci Rep 2022; 12:19094. [PMID: 36352007 PMCID: PMC9646783 DOI: 10.1038/s41598-022-23078-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2022] Open
Abstract
Antibody-mediated rejection (ABMR) is the leading cause of allograft failure in kidney transplantation. Defined by the Banff classification, its gold standard diagnosis remains a challenge, with limited inter-observer reproducibility of the histological scores and efficient immunomarker availability. We performed an immunohistochemical analysis of 3 interferon-related proteins, WARS1, TYMP and GBP1 in a cohort of kidney allograft biopsies including 17 ABMR cases and 37 other common graft injuries. Slides were interpreted, for an ABMR diagnosis, by four blinded nephropathologists and by a deep learning framework using convolutional neural networks. Pathologists identified a distinctive microcirculation staining pattern in ABMR with all three antibodies, displaying promising diagnostic performances and a substantial reproducibility. The deep learning analysis supported the microcirculation staining pattern and achieved similar diagnostic performance from internal validation, with a mean area under the receiver operating characteristic curve of 0.89 (± 0.02) for WARS1, 0.80 (± 0.04) for TYMP and 0.89 (± 0.04) for GBP1. The glomerulitis and peritubular capillaritis scores, the hallmarks of histological ABMR, were the most highly correlated Banff scores with the deep learning output, whatever the C4d status. These novel immunomarkers combined with a CNN framework could help mitigate current challenges in ABMR diagnosis and should be assessed in larger cohorts.
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21
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Zhang Y, Wang Y, Zhang B, Li Q. A hyperspectral dataset of precancerous lesions in gastric cancer and benchmarks for pathological diagnosis. JOURNAL OF BIOPHOTONICS 2022; 15:e202200163. [PMID: 35869783 DOI: 10.1002/jbio.202200163] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/24/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
Gastric cancer (GC) is one of the most common cancers worldwide. A lot of studies have found that early GC has good prognosis. Unfortunately, the diagnosis rate of early GC is suboptimal due to inadequate disease screening and the insidious nature of early lesions. Pathological diagnosis is usually regarded as the "gold standard" for the diagnosis of GC. However, traditional pathological diagnosis is tedious and time-consuming. With the development of deep learning, computer-aided diagnosis is widely used to assist pathologists for diagnosis. As conventional pathology, diagnosis is based on color images, it is not as informative as hyperspectral imaging, which introduces spectroscopy into imaging techniques. This article combines microscopic hyperspectral image (HSI) with deep learning networks to assist in the diagnosis of precancerous lesions in gastric cancer (PLGC). A large scale microscopic hyperspectral PLGC dataset with 924 effective scenes is built and self-supervised learning is adopted to provide pretrained models for HSI. These pretrained models effectively improve the performance of downstream classification tasks. Furthermore, a symmetrically deep connected network is proposed to train with images from different imaging modalities and improve the diagnostic accuracy to 96.59%.
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Affiliation(s)
- Ying Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
| | - Yan Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Center of SHMEC for Space Information and GNSS, Shanghai, China
| | - Benyan Zhang
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingli Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China
- Engineering Research Center of Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University, Shanghai, China
- Engineering Center of SHMEC for Space Information and GNSS, Shanghai, China
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22
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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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23
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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24
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Hara S, Haneda E, Kawakami M, Morita K, Nishioka R, Zoshima T, Kometani M, Yoneda T, Kawano M, Karashima S, Nambo H. Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules. PLoS One 2022; 17:e0271161. [PMID: 35816495 PMCID: PMC9273082 DOI: 10.1371/journal.pone.0271161] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/27/2022] [Indexed: 12/02/2022] Open
Abstract
Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.
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Affiliation(s)
- Satoshi Hara
- Medical Education Research Center, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Emi Haneda
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Masaki Kawakami
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Kento Morita
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Ryo Nishioka
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Takeshi Zoshima
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Mitsuhiro Kometani
- Department of Endocrinology and Metabolism, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Takashi Yoneda
- Department of Endocrinology and Metabolism, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
- Department of Health Promotion and Medicine of the Future, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
- Faculty of Transdisciplinary Sciences, Institute of Transdisciplinary Sciences, Kanazawa University, Kanazawa, Japan
| | - Mitsuhiro Kawano
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
- * E-mail: (MK); (HN)
| | | | - Hidetaka Nambo
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
- * E-mail: (MK); (HN)
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25
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Bülow RD, Marsh JN, Swamidass SJ, Gaut JP, Boor P. The potential of artificial intelligence-based applications in kidney pathology. Curr Opin Nephrol Hypertens 2022; 31:251-257. [PMID: 35165248 PMCID: PMC9035059 DOI: 10.1097/mnh.0000000000000784] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice. RECENT FINDINGS Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology. SUMMARY AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.
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Affiliation(s)
- Roman D. Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Jon N. Marsh
- Washington University School of Medicine in St. Louis, Department of Pathology and Immunology
| | - S. Joshua Swamidass
- Washington University School of Medicine in St. Louis, Department of Pathology and Immunology
| | - Joseph P. Gaut
- Washington University School of Medicine in St. Louis, Department of Pathology and Immunology
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
- Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany
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