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Liu X, Fang H, Liang D, Lei Q, Wang J, Xu F, Liang S, Liang D, Yang F, Li H, Chen J, Ni Y, Xie G, Zeng C. Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients. Ren Fail 2024; 46:2322043. [PMID: 38425049 PMCID: PMC10911252 DOI: 10.1080/0886022x.2024.2322043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND The analytical renal pathology system (ARPS) based on convolutional neural networks has been used successfully in native IgA nephropathy (IgAN) patients. Considering the similarity of pathologic features, we aim to evaluate the performance of the ARPS in allograft IgAN patients and broaden its implementation. METHODS Biopsy-proven allograft IgAN patients from two different centers were enrolled for internal and external validation. We implemented the ARPS to identify glomerular lesions and intrinsic glomerular cells, and then evaluated its performance. Consistency between the ARPS and pathologists was assessed using intraclass correlation coefficients. The association of digital pathological features with clinical and pathological data was measured. Kaplan-Meier survival curve and cox proportional hazards model were applied to investigate prognosis prediction. RESULTS A total of 56 biopsy-proven allograft IgAN patients from the internal center and 17 biopsy-proven allograft IgAN patients from the external center were enrolled in this study. The ARPS was successfully applied to identify the glomerular lesions (F1-score, 0.696-0.959) and quantify intrinsic glomerular cells (F1-score, 0.888-0.968) in allograft IgAN patients rapidly and precisely. Furthermore, the mesangial hypercellularity score was positively correlated with all mesangial metrics provided by ARPS [Spearman's correlation coefficient (r), 0.439-0.472, and all p values < 0.001]. Besides, a higher allograft survival was noticed among patients in the high-level groups of the maximum and ratio of endothelial cells, as well as the maximum and density of podocytes. CONCLUSION We propose that the ARPS could be implemented in future clinical practice with outstanding capability.
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Affiliation(s)
- Xumeng Liu
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Huiwen Fang
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Dongmei Liang
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qunjuan Lei
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | | | - Feng Xu
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Shaoshan Liang
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Dandan Liang
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Fan Yang
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Heng Li
- Kidney Disease Center, First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China
| | - Jianghua Chen
- Kidney Disease Center, First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, China
| | - Yuan Ni
- Ping An Healthcare Technology, Shanghai, China
| | - Guotong Xie
- Ping An Healthcare Technology, Shanghai, China
| | - Caihong Zeng
- National Clinical Research Center for Kidney Diseases, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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3
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Wu Z, Kondo A, McGrady M, Baker EAG, Chidester B, Wu E, Rahim MK, Bracey NA, Charu V, Cho RJ, Cheng JB, Afkarian M, Zou J, Mayer AT, Trevino AE. Discovery and generalization of tissue structures from spatial omics data. CELL REPORTS METHODS 2024; 4:100838. [PMID: 39127044 DOI: 10.1016/j.crmeth.2024.100838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/15/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024]
Abstract
Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.
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Affiliation(s)
- Zhenqin Wu
- Enable Medicine, Menlo Park, CA 94025, USA.
| | | | | | | | | | - Eric Wu
- Enable Medicine, Menlo Park, CA 94025, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | - Nathan A Bracey
- Institute of Immunity, Transplantation and Infection, Stanford University, Stanford, CA 94305, USA
| | - Vivek Charu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Raymond J Cho
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey B Cheng
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, USA; Department of Dermatology, Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Maryam Afkarian
- Division of Nephrology, Department of Medicine, University of California, Davis, Davis, CA 95618, USA
| | - James Zou
- Enable Medicine, Menlo Park, CA 94025, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
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4
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Wu R, Chen Z, Yu J, Lai P, Chen X, Han A, Xu M, Fan Z, Cheng B, Jiang Y, Xia J. A graph-learning based model for automatic diagnosis of Sjögren's syndrome on digital pathological images: a multicentre cohort study. J Transl Med 2024; 22:748. [PMID: 39118142 PMCID: PMC11308146 DOI: 10.1186/s12967-024-05550-8] [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: 02/05/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations. METHODS The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts. FINDINGS CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM's diagnostic accuracy is closer to skilled pathologists compared to beginners. INTERPRETATION Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.
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Affiliation(s)
- Ruifan Wu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Zhipei Chen
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Jiali Yu
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Peng Lai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Xuanyi Chen
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Anjia Han
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Meng Xu
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Zhaona Fan
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Bin Cheng
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Ying Jiang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
| | - Juan Xia
- Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University, Guangzhou, China.
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5
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Hölscher DL, Bülow RD. Decoding pathology: the role of computational pathology in research and diagnostics. Pflugers Arch 2024:10.1007/s00424-024-03002-2. [PMID: 39095655 DOI: 10.1007/s00424-024-03002-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/25/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.
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Affiliation(s)
- David L Hölscher
- Department for Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute for Pathology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Roman D Bülow
- Institute for Pathology, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany.
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6
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Xiang Y, Wang Q, Li M, Li Y, Yan W, Li Y, Dong J, Liu Y. Protective effects of dietary additive quercetin: Nephrotoxicity and ferroptosis induced by avermectin pesticide. Toxicon 2024; 246:107789. [PMID: 38843999 DOI: 10.1016/j.toxicon.2024.107789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/29/2024] [Indexed: 06/10/2024]
Abstract
In recent years, contamination of aquatic systems with Avermectin (AVM) has emerged as a significant concern. This contamination poses substantial challenges to freshwater aquaculture. Plant-derived Quercetin (QUE), known for its anti-inflammatory, antioxidant, and ferroptosis-inhibiting properties, is commonly employed as a supplement in animal feed. However, its protective role against chronic renal injury in freshwater carp induced by AVM remains unclear. This study assesses the influence of dietary supplementation with QUE on the consequences of chronic AVM exposure on carp renal function. The carp were subjected to a 30-day exposure to AVM and were provided with a diet containing 400 mg/kg of QUE. Pathological observations indicated that QUE alleviated renal tissue structural damage caused by AVM. RT-QPCR study revealed that QUE effectively reduced the increased expression levels of pro-inflammatory factors mRNA produced by AVM exposure, by concurrently raising the mRNA expression level of the anti-inflammatory factor. Quantitative analysis using DHE tests and biochemical analysis demonstrated that QUE effectively reduced the buildup of ROS in the renal tissues of carp, activity of antioxidant enzymes CAT, SOD, and GSH-px, which were inhibited by AVM, and increased the content of GSH, which was induced by prolonged exposure to AVM. QUE also reduced the levels of MDA, a marker of oxidative damage. Furthermore, assays for ferroptosis markers indicated that QUE increased the mRNA expression levels of gpx4 and slc7a11, which were reduced due to AVM induction, and it caused a reduction in the mRNA expression levels of ftl, ncoa4, and cox2, along with a drop in the Fe2+ concentration. In summary, QUE mitigates chronic AVM exposure-induced renal inflammation in carp by inhibiting the transcription of pro-inflammatory cytokines. By blocking ROS accumulation, renal redox homeostasis is restored, thereby inhibiting renal inflammation and ferroptosis. This provides a theoretical basis for the development of freshwater carp feed formula.
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Affiliation(s)
- Yannan Xiang
- Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Marine Pharmaceutical Resources Development Engineering Research Center, Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China
| | - Qiao Wang
- Department of Pathology, The First People's Hospital of Lianyungang, Lianyungang, 222005, Jiangsu, China
| | - Mengxin Li
- Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Marine Pharmaceutical Resources Development Engineering Research Center, Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China
| | - Ying Li
- Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Marine Pharmaceutical Resources Development Engineering Research Center, Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China
| | - Weiping Yan
- Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Marine Pharmaceutical Resources Development Engineering Research Center, Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China
| | - Yuanyuan Li
- Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Marine Pharmaceutical Resources Development Engineering Research Center, Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China
| | - Jingquan Dong
- Jiangsu Key Laboratory of Marine Bioresources and Environment, Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Marine Pharmaceutical Resources Development Engineering Research Center, Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang, 222005, China.
| | - Yi Liu
- Department of Pathology, The First People's Hospital of Lianyungang, Lianyungang, 222005, Jiangsu, China.
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Feng D, Shi X, Wang J, Zhang L, Xiao Y, Li D, Wu R, Wei W, Miyamoto A, Yoo KH, Ye X, Zhang C, Han P. Telemedicine-medical "snack community"-PHS ecosystem: Insights into the double-edged sword role of telemedicine in clinical practice and medical education during the COVID-19 pandemic and beyond. EXPLORATION (BEIJING, CHINA) 2024; 4:20230111. [PMID: 39175890 PMCID: PMC11335463 DOI: 10.1002/exp.20230111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/07/2023] [Indexed: 08/24/2024]
Abstract
Telemedicine has gained tremendous development during the COVID-19 pandemic. With deblocking and opening, telemedicine accelerates the evolvution of the medical "snack community" and undermines the perception of medical students and staff, which promotes the incidence of psychosocial-related disorders. Moreover, the inconsistent telemedicine adaptability between medical workers and patients aggravates the doctor-patient conflict due to the aging population and COVID-19 squeal. Telemedicine is colliding with the national healthcare system, whose synchronization with conventional medical service is crucial to coordinate the relationship among medical payment, patient privacy and qualifications of clinicians. This study puts more emphasis on the double-edged sword role of telemedicine in clinical practice and medical education during the COVID-19 pandemic and beyond. Overall, while telemedicine has demonstrated its utility in health care throughout the COVID pandemic, it is pretty critical to continue evaluating the efficacy and limitations of telemedicine in order to maintain equal access to medical service and high-quality medical education. A new concept as telemedicine-medical "snack community"-PHS ecosystem, where the psychological health education system and partners healthcare system with enough bandwidth, especially 5G technology, could optimize the effect of telemedicine on medical practice and education, is proposed.
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Affiliation(s)
- Dechao Feng
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
- Department of RehabilitationThe Affiliated Hospital of Southwest Medical UniversityLuzhouP. R. China
| | - Xu Shi
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Jie Wang
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Liying Zhang
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Yuhan Xiao
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Dengxiong Li
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Ruicheng Wu
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Wuran Wei
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
| | - Akira Miyamoto
- Department of RehabilitationWest Kyushu UniversityFukuokaJapan
| | - Koo Han Yoo
- Department of UrologyKyung Hee UniversitySeoulSouth Korea
| | - Xing Ye
- Cedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Chi Zhang
- Department of RehabilitationThe Affiliated Hospital of Southwest Medical UniversityLuzhouP. R. China
| | - Ping Han
- Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduChina
- West China School of medicineWest China HospitalSichuan UniversityChengduChina
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8
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Meng Y, Zhao M, Ma Q, Hua Q, Hu J, Zhou Q, Yi H, Zhang Z, Zhang L. Bifidobacterium bifidum alleviates adenine-induced acute kidney injury in mice by improving intestinal barrier function. Food Funct 2024; 15:8030-8042. [PMID: 38984966 DOI: 10.1039/d4fo02014f] [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: 07/11/2024]
Abstract
Acute kidney injury (AKI) is a kind of critical kidney disease characterized by tubular injury, rapid decline of renal function and renal inflammation, with high clinical incidence. AKI has been shown to be associated with dysregulation of the gut microbiota and impaired intestinal barrier. Bifidobacterium has a positive impact on the treatment of many diseases. However, little is known about the role and mechanism of Bifidobacterium in AKI. Based on previous experiments, Bifidobacterium bifidum FL228.1 and FL276.1, which can relieve intestinal inflammation, and Bifidobacterium bifidum ZL.1, which has anti-inflammatory potential, were screened. This study aimed to investigate the effects of Bifidobacterium bifidum FL228.1, FL276.1 and ZL.1 on AKI, focusing on their role in the gut microbiota composition and intestinal barrier function. Our results showed that Bifidobacterium bifidum FL228.1, FL276.1 and ZL.1 effectively improved kidney function in mice with AKI by regulating the gut microbiota dysregulation, inhibiting intestinal inflammation and rebuilding the intestinal mucosal barrier. In addition, intervention with probiotics turned the gut microbiota disturbance caused by AKI into a normalized trend, reversed the adverse outcome of microbiota imbalance, and increased the abundance of potentially beneficial bacteria Bifidobacterium and Faecalibaculum. In summary, Bifidobacterium bifidum FL228.1, FL276.1, and ZL.1 alleviate adenine-induced AKI based on the gut-kidney axis. Although their mechanisms of action are different, their effect on alleviating AKI is almost the same.
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Affiliation(s)
- Yang Meng
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Maozhen Zhao
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Qiyu Ma
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Qinglian Hua
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Jinpeng Hu
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Qi Zhou
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Huaxi Yi
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Zhe Zhang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
| | - Lanwei Zhang
- College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China.
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Tasca P, van den Berg BM, Rabelink TJ, Wang G, Heijs B, van Kooten C, de Vries APJ, Kers J. Application of spatial-omics to the classification of kidney biopsy samples in transplantation. Nat Rev Nephrol 2024:10.1038/s41581-024-00861-x. [PMID: 38965417 DOI: 10.1038/s41581-024-00861-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2024] [Indexed: 07/06/2024]
Abstract
Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.
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Affiliation(s)
- Paola Tasca
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Bernard M van den Berg
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ton J Rabelink
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (Renew), Leiden University Medical Center, Leiden, the Netherlands
| | - Gangqi Wang
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (Renew), Leiden University Medical Center, Leiden, the Netherlands
| | - Bram Heijs
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Cees van Kooten
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Aiko P J de Vries
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Internal Medicine, Division of Nephrology, Einthoven Laboratory of Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, the Netherlands.
| | - Jesper Kers
- Leiden Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
- Center for Analytical Sciences Amsterdam, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
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10
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Juang CF, Chuang YW, Lin GW, Chung IF, Lo YC. Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images. Comput Med Imaging Graph 2024; 115:102375. [PMID: 38599040 DOI: 10.1016/j.compmedimag.2024.102375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/01/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024]
Abstract
Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.
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Affiliation(s)
- Chia-Feng Juang
- Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan, ROC
| | - Ya-Wen Chuang
- Section of Nephrology, Department of Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan, ROC; Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung 406040, Taiwan, ROC; School of Medicine, College of Medicine, China Medical University, Taichung 406040, Taiwan, ROC; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 40227, Taiwan, ROC
| | - Guan-Wen Lin
- Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan, ROC
| | - I-Fang Chung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan, ROC.
| | - Ying-Chih Lo
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA.
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11
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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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12
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Chen J, Chen R, Chen L, Zhang L, Wang W, Zeng X. Kidney medicine meets computer vision: a bibliometric analysis. Int Urol Nephrol 2024:10.1007/s11255-024-04082-w. [PMID: 38814370 DOI: 10.1007/s11255-024-04082-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. METHODS The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. RESULTS There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. CONCLUSION The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.
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Affiliation(s)
- Junren Chen
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Rui Chen
- The Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
| | - Liangyin Chen
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Lei Zhang
- School of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Wei Wang
- School of Automation, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China
| | - Xiaoxi Zeng
- Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
- Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China.
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13
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Pilva P, Bülow R, Boor P. Deep learning applications for kidney histology analysis. Curr Opin Nephrol Hypertens 2024; 33:291-297. [PMID: 38411024 DOI: 10.1097/mnh.0000000000000973] [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: 02/28/2024]
Abstract
PURPOSE OF REVIEW Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives. RECENT FINDINGS Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios. SUMMARY Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.
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Affiliation(s)
| | | | - Peter Boor
- Institute of Pathology
- Department of Nephrology and Clinical Immunology, RWTH Aachen University Hospital, Aachen, Germany
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14
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Lei Q, Hou X, Liu X, Liang D, Fan Y, Xu F, Liang S, Liang D, Yang J, Xie G, Liu Z, Zeng C. Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy. J Transl Med 2024; 22:397. [PMID: 38684996 PMCID: PMC11059590 DOI: 10.1186/s12967-024-05221-8] [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: 11/12/2023] [Accepted: 04/19/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed. METHODS A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed. RESULTS Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function. CONCLUSION Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.
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Affiliation(s)
- Qunjuan Lei
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Xiaoshuai Hou
- Ping An Healthcare Technology, 206 Kaibin Road, Shanghai, 200030, China
| | - Xumeng Liu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Dongmei Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Yun Fan
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Feng Xu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Shaoshan Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Dandan Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Jing Yang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China
| | - Guotong Xie
- Ping An Healthcare Technology, 206 Kaibin Road, Shanghai, 200030, China.
- Ping An Healthcare and Technology Company Limited, Shanghai, China.
- Ping An International Smart City Technology Co., Shanghai, China.
| | - Zhihong Liu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China.
| | - Caihong Zeng
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, 305 East Zhongshan Road, Nanjing, 210009, China.
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15
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Nguyen TTU, Nguyen AT, Kim H, Jung YJ, Park W, Kim KM, Park I, Kim W. Deep-learning model for evaluating histopathology of acute renal tubular injury. Sci Rep 2024; 14:9010. [PMID: 38637573 PMCID: PMC11026462 DOI: 10.1038/s41598-024-58506-9] [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: 08/08/2023] [Accepted: 03/30/2024] [Indexed: 04/20/2024] Open
Abstract
Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models to assist in the histopathological diagnosis of acute kidney injury. This study aimed to perform histopathological segmentation to identify the four structures of acute renal tubular injury using deep-learning models. A segmentation model was used to classify tubule-specific injuries following cisplatin treatment. A total of 45 whole-slide images with 400 generated patches were used in the segmentation model, and 27,478 annotations were created for four classes: glomerulus, healthy tubules, necrotic tubules, and tubules with casts. A segmentation model was developed using the DeepLabV3 architecture with a MobileNetv3-Large backbone to accurately identify the four histopathological structures associated with acute renal tubular injury in PAS-stained mouse samples. In the segmentation model for four structures, the highest Intersection over Union and the Dice coefficient were obtained for the segmentation of the "glomerulus" class, followed by "necrotic tubules," "healthy tubules," and "tubules with cast" classes. The overall performance of the segmentation algorithm for all classes in the test set included an Intersection over Union of 0.7968 and a Dice coefficient of 0.8772. The Dice scores for the glomerulus, healthy tubules, necrotic tubules, and tubules with cast are 91.78 ± 11.09, 87.37 ± 4.02, 88.08 ± 6.83, and 83.64 ± 20.39%, respectively. The utilization of deep learning in a predictive model has demonstrated promising performance in accurately identifying the degree of injured renal tubules. These results may provide new opportunities for the application of the proposed methods to evaluate renal pathology more effectively.
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Affiliation(s)
- Thi Thuy Uyen Nguyen
- Department of Histology, Embryology, Pathology and Forensic Medicine, Hue University of Medicine and Pharmacy, Hue University, Hue City, Vietnam
| | - Anh-Tien Nguyen
- Department of Radiology, Chonnam National University and Hospital, Gwangju, Korea
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Hyeongwan Kim
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea
| | - Yu Jin Jung
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea
| | - Woong Park
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea
| | - Kyoung Min Kim
- Department of Pathology, Jeonbuk National University Medical School, Jeonju, Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University and Hospital, Gwangju, Korea.
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea.
- Department of Data Science, Chonnam National University, Gwangju, Korea.
| | - Won Kim
- Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, Republic of Korea.
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute, Jeonju, Republic of Korea.
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16
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Son R, Yamazawa K, Oguchi A, Suga M, Tamura M, Yanagita M, Murakawa Y, Kume S. Morphomics via next-generation electron microscopy. J Mol Cell Biol 2024; 15:mjad081. [PMID: 38148118 PMCID: PMC11167312 DOI: 10.1093/jmcb/mjad081] [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: 03/22/2022] [Revised: 10/02/2022] [Accepted: 12/23/2023] [Indexed: 12/28/2023] Open
Abstract
The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged, enabling coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.
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Affiliation(s)
- Raku Son
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kenji Yamazawa
- Advanced Manufacturing Support Team, RIKEN Center for Advanced Photonics, Wako 351-0198, Japan
| | - Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Mitsuo Suga
- Multimodal Microstructure Analysis Unit, RIKEN–JEOL Collaboration Center, Kobe 650-0047, Japan
| | - Masaru Tamura
- Technology and Development Team for Mouse Phenotype Analysis, RIKEN BioResource Research Center, Tsukuba 305-0074, Japan
| | - Motoko Yanagita
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
- IFOM—The FIRC Institute of Molecular Oncology, Milan 20139, Italy
| | - Satoshi Kume
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan
- Center for Health Science Innovation, Osaka City University, Osaka 530-0011, Japan
- Osaka Electro-Communication University, Neyagawa 572-8530, Japan
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17
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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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18
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Nachiappa Ganesh R, Graviss EA, Nguyen D, El-Zaatari Z, Gaber L, Barrios R, Truong L, Farris AB. Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology. Hum Pathol 2024; 146:75-85. [PMID: 38640986 DOI: 10.1016/j.humpath.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 04/21/2024]
Abstract
INTRODUCTION Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of interobserver variability in its prognostic utility, generating a need for computerized quantification. METHODS We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods. A standard definition diagram was agreed after international consultation and four renal pathologists scored each parameter in a double-blinded manner. Interstitial fibrosis (IF) score was assessed with five different computerized and AI-based algorithms on trichrome and PAS stains. RESULTS There was strong prognostic correlation with renal function and graft outcome at a median follow-up ranging from 24 to 42 months respectively, independent of moderate concordance for pathologists scores. IF scores with two of the computerized algorithms showed significant correlation with estimated glomerular filtration rate (eGFR) at biopsy but not at the end of follow-up. There was poor concordance for AI based platforms. CONCLUSION Chronicity scores are robust prognostic tools despite interobserver reproducibility. AI-algorithms have absolute precision but are limited by significant variation when different hardware and software algorithms are used for quantification.
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Affiliation(s)
- Rajesh Nachiappa Ganesh
- Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA.
| | - Edward A Graviss
- Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA; J.C. Walter Jr. Transplant Center, Department of Surgery, Houston, TX, USA
| | - Duc Nguyen
- Department of Pediatrics, Baylor College of Medicine, USA.
| | - Ziad El-Zaatari
- Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Lillian Gaber
- Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA; J.C. Walter Jr. Transplant Center, Department of Surgery, Houston, TX, USA
| | - Roberto Barrios
- Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Luan Truong
- Department of Pathology and Genomic Medicine, The Houston Methodist Hospital and Research Institute, Houston, TX, USA
| | - Alton B Farris
- Department of Pathology and Laboratory Medicine, Emory University Hospital, Atlanta, GA, USA
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19
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Alexander MP, Zaidi M, Larson N, Mullan A, Pavelko KD, Stegall MD, Bentall A, Wouters BG, McKee T, Taner T. Exploring the single-cell immune landscape of kidney allograft inflammation using imaging mass cytometry. Am J Transplant 2024; 24:549-563. [PMID: 37979921 DOI: 10.1016/j.ajt.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/01/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and the immune landscape. Here, using imaging mass cytometry with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy, and chronic pyelonephritis). Using novel analytical tools, for cell segmentation, we segmented over 900 000 cells and developed a tissue-based classifier using over 3000 manually annotated kidney microstructures (glomeruli, tubules, interstitium, and arteries). Using PhenoGraph, we identified 11 immune and 9 nonimmune clusters and found a high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate between the different allograft inflammatory phenotypes. Further validation of imaging mass cytometry in larger cohorts and with more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.
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Affiliation(s)
- Mariam P Alexander
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, Minnesota, USA.
| | - Mark Zaidi
- Department of Medical Biophysics, University of Toronto, Canada
| | - Nicholas Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Aidan Mullan
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kevin D Pavelko
- Immune Monitoring Core Laboratory, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- Departments of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew Bentall
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA
| | - Bradly G Wouters
- Department of Medical Biophysics, University of Toronto, Canada; Princess Margaret Cancer Center, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Trevor McKee
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada; Pathomics Inc., Toronto, Ontario, Canada
| | - Timucin Taner
- Departments of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota, USA
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20
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Wei X, Long M, Fan Z, Hou Y, Zhu X, Qu Z, Du Y. Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors. Front Endocrinol (Lausanne) 2024; 15:1328579. [PMID: 38524629 PMCID: PMC10958378 DOI: 10.3389/fendo.2024.1328579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/26/2024] [Indexed: 03/26/2024] Open
Abstract
Background Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN) exhibit varying sensitivities to immunotherapy. This study aimed to establish and validate a model incorporating pathological and clinical features using deep learning training to evaluate the response of patients with IMN to immunosuppressive therapy. Methods The 291 patients were randomly categorized into training (n = 219) and validation (n = 72) cohorts. Patch-level convolutional neural network training in a weakly supervised manner was utilized to analyze whole-slide histopathological features. We developed a machine-learning model to assess the predictive value of pathological signatures compared to clinical factors. The performance levels of the models were evaluated using the area under the receiver operating characteristic curve (AUC) on the training and validation tests, and the prediction accuracies of the models for immunotherapy response were compared. Results Multivariate analysis indicated that diabetes and smoking were independent risk factors affecting the response to immunotherapy in IMN patients. The model integrating pathologic features had a favorable predictive value for determining the response to immunotherapy in IMN patients, with AUCs of 0.85 and 0.77 when employed in the training and test cohorts, respectively. However, when incorporating clinical features into the model, the predictive efficacy diminishes, as evidenced by lower AUC values of 0.75 and 0.62 on the training and testing cohorts, respectively. Conclusions The model incorporating pathological signatures demonstrated a superior predictive ability for determining the response to immunosuppressive therapy in IMN patients compared to the integration of clinical factors.
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Affiliation(s)
| | | | | | | | | | - Zhihui Qu
- *Correspondence: Zhihui Qu, ; Yujun Du,
| | - Yujun Du
- *Correspondence: Zhihui Qu, ; Yujun Du,
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21
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Bülow RD, Droste P, Boor P. [Advances in computational quantitative nephropathology]. PATHOLOGIE (HEIDELBERG, GERMANY) 2024; 45:140-145. [PMID: 38308066 DOI: 10.1007/s00292-024-01300-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 02/04/2024]
Abstract
BACKGROUND Semiquantitative histological scoring systems are frequently used in nephropathology. In computational nephropathology, the focus is on generating quantitative data from histology (so-called pathomics). Several recent studies have collected such data using next-generation morphometry (NGM) based on segmentations by artificial neural networks and investigated their usability for various clinical or diagnostic purposes. AIM To present an overview of the current state of studies regarding renal pathomics and to identify current challenges and potential solutions. MATERIALS AND METHODS Due to the literature restriction (maximum of 30 references), studies were selected based on a database search that processed as much data as possible, used innovative methodologies, and/or were ideally multicentric in design. RESULTS AND DISCUSSION Pathomics studies in the kidney have impressively demonstrated that morphometric data are useful clinically (for example, for prognosis assessment) and translationally. Further development of NGM requires overcoming some challenges, including better standardization and generation of prospective evidence.
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Affiliation(s)
- Roman D Bülow
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland
| | - Patrick Droste
- Institut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland
- Medizinische Klinik II, Universitätsklinikum RWTH Aachen, Aachen, 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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22
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Emmons BR, Batal I, King KL, Yu M, Canetta PA, Sandoval PR, Mohan S, Tsapepas D, Adler JT, Ratner LE, Husain SA. Association of Implantation Biopsy Findings in Living Donor Kidneys With Donor and Recipient Outcomes. Am J Kidney Dis 2024; 83:306-317. [PMID: 37879529 PMCID: PMC10922703 DOI: 10.1053/j.ajkd.2023.08.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/31/2023] [Accepted: 08/28/2023] [Indexed: 10/27/2023]
Abstract
RATIONALE & OBJECTIVE Some living donor kidneys are found to have biopsy evidence of chronic scarring and/or glomerular disease at implantation, but it is unclear if these biopsy findings help predict donor kidney recovery or allograft outcomes. Our objective was to identify the prevalence of chronic histological changes and glomerular disease in donor kidneys, and their association with donor and recipient outcomes. STUDY DESIGN Retrospective cohort study. SETTING & PARTICIPANTS Single center, living donor kidney transplants from January 2010 to July 2022. EXPOSURE Chronic histological changes, glomerular disease in donor kidney implantation biopsies. OUTCOME For donors, single-kidney estimated glomerular filtration rate (eGFR) increase, percent total eGFR loss, ≥40% eGFR decline from predonation baseline, and eGFR<60mL/min/1.73m2 at 6 months after donation; for recipients, death-censored allograft survival. ANALYTICAL APPROACH Biopsies were classified as having possible glomerular disease by pathologist diagnosis or chronic changes based on the percentage of glomerulosclerosis, interstitial fibrosis/tubular atrophy, and vascular disease. We used logistic regression to identify factors associated with the presence of chronic changes, linear regression to identify the association between chronic changes and single-kidney estimated glomerular filtration rate (eGFR) recovery, and time-to-event analyses to identify the relationship between abnormal biopsy findings and allograft outcomes. RESULTS Among 1,104 living donor kidneys, 155 (14%) had advanced chronic changes on implantation biopsy, and 12 (1%) had findings suggestive of possible donor glomerular disease. Adjusted logistic regression showed that age (odds ratio [OR], 2.44 per 10 years [95% CI, 1.98-3.01), Hispanic ethnicity (OR, 1.87 [95% CI, 1.15-3.05), and hypertension (OR, 1.92 [95% CI, 1.01-3.64), were associated with higher odds of chronic changes on implantation biopsy. Adjusted linear regression showed no association of advanced chronic changes with single-kidney eGFR increase or relative risk of eGFR<60mL/min/1.73m2. There were no differences in time-to-death-censored allograft failure in unadjusted or adjusted Cox proportional hazards models when comparing kidneys with chronic changes to kidneys without histological abnormalities. LIMITATIONS Retrospective, absence of measured GFR. CONCLUSIONS Approximately 1 in 7 living donor kidneys had chronic changes on implantation biopsy, primarily in the form of moderate vascular disease, and 1% had possible donor glomerular disease. Abnormal implantation biopsy findings were not significantly associated with 6-month donor eGFR outcomes or allograft survival. PLAIN-LANGUAGE SUMMARY Kidney biopsies are the gold standard test to identify the presence or absence of kidney disease. However, kidneys donated by healthy living donors-who are extensively screened for any evidence of kidney disease before donation-occasionally show findings that might be considered "abnormal," including the presence of scarring in the kidney or findings suggestive of a primary kidney disease. We studied the frequency of abnormal kidney biopsy findings among living donors at our center. We found that about 14% of kidneys had chronic abnormalities and 1% had findings suggesting possible glomerular kidney disease, but the presence of abnormal biopsy findings was not associated with worse outcomes for the donors or their recipients.
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Affiliation(s)
- Brendan R Emmons
- Department of Medicine, Division of Nephrology, Columbia University, New York, New York; Columbia University Renal Epidemiology Group, New York, New York
| | - Ibrahim Batal
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Kristen L King
- Department of Medicine, Division of Nephrology, Columbia University, New York, New York; Columbia University Renal Epidemiology Group, New York, New York
| | - Miko Yu
- Department of Medicine, Division of Nephrology, Columbia University, New York, New York; Columbia University Renal Epidemiology Group, New York, New York
| | - Pietro A Canetta
- Department of Medicine, Division of Nephrology, Columbia University, New York, New York
| | | | - Sumit Mohan
- Department of Medicine, Division of Nephrology, Columbia University, New York, New York; Columbia University Renal Epidemiology Group, New York, New York; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Demetra Tsapepas
- Department of Transplant Analytics, New York-Presbyterian Hospital, New York, New York
| | - Joel T Adler
- Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, Texas
| | - Lloyd E Ratner
- Department of Surgery, Columbia University, New York, New York
| | - S Ali Husain
- Department of Medicine, Division of Nephrology, Columbia University, New York, New York; Columbia University Renal Epidemiology Group, New York, New York.
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23
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El-Achkar TM, Eadon MT, Kretzler M, Himmelfarb J. Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project. Am J Kidney Dis 2024; 83:402-410. [PMID: 37839688 PMCID: PMC10922684 DOI: 10.1053/j.ajkd.2023.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 10/17/2023]
Abstract
Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.
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Affiliation(s)
- Tarek M El-Achkar
- Division of Nephrology, School of Medicine, Indiana University, and Richard L. Roudebush Veteran Affairs Medical Center, Indianapolis, Indiana
| | - Michael T Eadon
- Division of Nephrology, School of Medicine, Indiana University, and Richard L. Roudebush Veteran Affairs Medical Center, Indianapolis, Indiana
| | - Matthias Kretzler
- Department of Computational Medicine & Bioinformatics, and Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jonathan Himmelfarb
- Kidney Research Institute and Division of Nephrology, University of Washington, Seattle, Washington.
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24
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Chelebian E, Avenel C, Ciompi F, Wählby C. DEPICTER: Deep representation clustering for histology annotation. Comput Biol Med 2024; 170:108026. [PMID: 38308865 DOI: 10.1016/j.compbiomed.2024.108026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/24/2024] [Accepted: 01/24/2024] [Indexed: 02/05/2024]
Abstract
Automatic segmentation of histopathology whole-slide images (WSI) usually involves supervised training of deep learning models with pixel-level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully supervised segmentation requires large-scale data manually annotated by experts, which can be expensive and time-consuming to obtain. Non-fully supervised methods, ranging from semi-supervised to unsupervised, have been proposed to address this issue and have been successful in WSI segmentation tasks. But these methods have mainly been focused on technical advancements in algorithmic performance rather than on the development of practical tools that could be used by pathologists or researchers in real-world scenarios. In contrast, we present DEPICTER (Deep rEPresentatIon ClusTERing), an interactive segmentation tool for histopathology annotation that produces a patch-wise dense segmentation map at WSI level. The interactive nature of DEPICTER leverages self- and semi-supervised learning approaches to allow the user to participate in the segmentation producing reliable results while reducing the workload. DEPICTER consists of three steps: first, a pretrained model is used to compute embeddings from image patches. Next, the user selects a number of benign and cancerous patches from the multi-resolution image. Finally, guided by the deep representations, label propagation is achieved using our novel seeded iterative clustering method or by directly interacting with the embedding space via feature space gating. We report both real-time interaction results with three pathologists and evaluate the performance on three public cancer classification dataset benchmarks through simulations. The code and demos of DEPICTER are publicly available at https://github.com/eduardchelebian/depicter.
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Affiliation(s)
- Eduard Chelebian
- Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden.
| | - Chirstophe Avenel
- Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carolina Wählby
- Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden
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25
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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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26
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Takahashi K, Ozawa E, Shimakura A, Mori T, Miyaaki H, Nakao K. Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis. Diagnostics (Basel) 2024; 14:374. [PMID: 38396413 PMCID: PMC10887964 DOI: 10.3390/diagnostics14040374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Gallbladder (GB) disease is classified into two broad categories: GB wall-thickening and protuberant lesions, which include various lesions, such as adenomyomatosis, cholecystitis, GB polyps, and GB carcinoma. This review summarizes recent advances in the differential diagnosis of GB lesions, focusing primarily on endoscopic ultrasound (EUS) and related technologies. Fundamental B-mode EUS and contrast-enhanced harmonic EUS (CH-EUS) have been reported to be useful for the diagnosis of GB diseases because they can evaluate the thickening of the GB wall and protuberant lesions in detail. We also outline the current status of EUS-guided fine-needle aspiration (EUS-FNA) for GB lesions, as there have been scattered reports on EUS-FNA in recent years. Furthermore, artificial intelligence (AI) technologies, ranging from machine learning to deep learning, have become popular in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification. In this review, we outline the current status of AI in the diagnosis of GB.
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Affiliation(s)
- Kosuke Takahashi
- Department of Gastroenterology and Hepatology, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852-8501, Japan; (E.O.); (T.M.); (H.M.); (K.N.)
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27
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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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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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Roetzer-Pejrimovsky T, Nenning KH, Kiesel B, Klughammer J, Rajchl M, Baumann B, Langs G, Woehrer A. Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma. Gigascience 2024; 13:giae057. [PMID: 39185700 PMCID: PMC11345537 DOI: 10.1093/gigascience/giae057] [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: 10/20/2023] [Revised: 05/13/2024] [Accepted: 07/20/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. RESULTS We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). CONCLUSIONS We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.
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Affiliation(s)
- Thomas Roetzer-Pejrimovsky
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, 1090 Vienna, Austria
| | - Karl-Heinz Nenning
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY 10962, USA
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Johanna Klughammer
- Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 80539 Munich, Germany
| | - Martin Rajchl
- Department of Computing and Medicine, Imperial College London, London SW7 2AZ, UK
| | - Bernhard Baumann
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, 1090 Vienna, Austria
- Department of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria
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30
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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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31
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Huang YL, Liu XQ, Huang Y, Jin FY, Zhao Q, Wu QY, Ma KL. Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy. J Clin Pathol 2023:jcp-2023-209215. [PMID: 38123970 DOI: 10.1136/jcp-2023-209215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset of 'real' data, and in reality, it should be robust, user-friendly and universally applicable. METHODS WSIs of primary IgAN were collected and annotated. The H-AI-L algorithm which could facilitate direct WSI viewing and potential ROI detection for clinicians was built on the cloud server of matpool, a shared internet-based service platform. Model performance was evaluated using F1-score, precision, recall and Matthew's correlation coefficient (MCC). RESULTS The F1-score of glomerular localisation in WSIs was 0.85 and 0.89 for the initial and pretrained models, respectively, with corresponding recall values of 0.79 and 0.83, and precision scores of 0.92 and 0.97. Dichotomous differentiation between global sclerotic (GS) and other glomeruli revealed F1-scores of 0.70 and 0.91, and MCC values of 0.55 and 0.87, for the initial and pretrained models, respectively. The overall F1-score of multiclassification was 0.81 for the pretrained models. The total glomerular recall rate was 0.96, with F1-scores of 0.68, 0.56 and 0.26 for GS, segmental glomerulosclerosis and crescent (C), respectively. Interstitial fibrosis/tubular atrophy lesion similarity between the true label and model predictions was 0.75. CONCLUSIONS Our results underscore the efficacy of the ML integration algorithm in segmenting ROIs in IgAN WSIs, and the internet-based model deployment is in favour of widespread adoption and utilisation across multiple centres and increased volumes of WSIs.
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Affiliation(s)
- Yu-Lin Huang
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiao Qi Liu
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Huang
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Feng Yong Jin
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qing Zhao
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qin Yi Wu
- Institute of Nephrology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Kun Ling Ma
- Department of Nephrology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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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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Fayos De Arizón L, Viera ER, Pilco M, Perera A, De Maeztu G, Nicolau A, Furlano M, Torra R. Artificial intelligence: a new field of knowledge for nephrologists? Clin Kidney J 2023; 16:2314-2326. [PMID: 38046016 PMCID: PMC10689169 DOI: 10.1093/ckj/sfad182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Indexed: 12/05/2023] Open
Abstract
Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.
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Affiliation(s)
- Leonor Fayos De Arizón
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Elizabeth R Viera
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Melissa Pilco
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alexandre Perera
- Center for Biomedical Engineering Research (CREB), Universitat Politècnica de Barcelona (UPC), Barcelona, Spain; Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain; Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | | | | | - Monica Furlano
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Roser Torra
- Nephrology Department, Fundació Puigvert; Institut d'Investigacions Biomèdiques Sant Pau (IIB-Sant Pau); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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Frei AL, Oberson R, Baumann E, Perren A, Grobholz R, Lugli A, Dawson H, Abbet C, Lertxundi I, Reinhard S, Mookhoek A, Feichtinger J, Sarro R, Gadient G, Dommann-Scherrer C, Barizzi J, Berezowska S, Glatz K, Dertinger S, Banz Y, Schoenegg R, Rubbia-Brandt L, Fleischmann A, Saile G, Mainil-Varlet P, Biral R, Giudici L, Soltermann A, Chaubert AB, Stadlmann S, Diebold J, Egervari K, Bénière C, Saro F, Janowczyk A, Zlobec I. Pathologist Computer-Aided Diagnostic Scoring of Tumor Cell Fraction: A Swiss National Study. Mod Pathol 2023; 36:100335. [PMID: 37742926 DOI: 10.1016/j.modpat.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems.
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Affiliation(s)
- Ana Leni Frei
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
| | - Raphaël Oberson
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Elias Baumann
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Aurel Perren
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Rainer Grobholz
- Medical Faculty University of Zurich, Institute of Pathology, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Alessandro Lugli
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Heather Dawson
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Christian Abbet
- Signal Processing Laboratory 5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ibai Lertxundi
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Stefan Reinhard
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Aart Mookhoek
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | | | - Rossella Sarro
- Istituto Cantonale di Patologia, Ente ospedaliero cantonale (EOC), Locarno, Switzerland
| | | | | | - Jessica Barizzi
- Istituto Cantonale di Patologia, Ente ospedaliero cantonale (EOC), Locarno, Switzerland
| | - Sabina Berezowska
- Institute of Pathology, Lausanne University Hospital, Lausanne, Switzerland
| | - Katharina Glatz
- Institut of Pathology, University Hospital Basel, Basel, Switzerland
| | - Susanne Dertinger
- Institute of Pathology, Landeskrankenhaus Feldkirch, Feldkirch, Austria
| | - Yara Banz
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Rene Schoenegg
- Institute of Pathology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Laura Rubbia-Brandt
- Department of Pathology and Immunology, Geneva University Hospital, Genève, Switzerland
| | - Achim Fleischmann
- Institute of Pathology, Cantonal Hospital Thurgau, Münsterlingen, Switzerland
| | | | | | | | - Luca Giudici
- Istituto Cantonale di Patologia, Ente ospedaliero cantonale (EOC), Locarno, Switzerland
| | | | - Audrey Baur Chaubert
- FMH Pathology, Pathology Department of SYNLAB Switzerland SA, Lausanne, Switzerland
| | - Sylvia Stadlmann
- Institute of Pathology, Cantonal Hospital Baden, Baden, Switzerland
| | - Joachim Diebold
- Institute of Pathology, Cantonal Hospital Luzern, Luzern, Switzerland
| | - Kristof Egervari
- Department of Pathology and Immunology, Geneva University Hospital, Genève, Switzerland
| | | | - Francesca Saro
- Institute of Pathology and Molecular Pathology, University Hospital Zürich, Zürich, Switzerland
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Emory University, Atlanta, Georgia; Department of Oncology, Division of Precision Oncology, University Hospital of Geneva, Geneva, Switzerland; Department of Clinical Pathology, Division of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Inti Zlobec
- Institute for Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
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Xia P, Lv Z, Wen Y, Zhang B, Zhao X, Zhang B, Wang Y, Cui H, Wang C, Zheng H, Qin Y, Sun L, Ye N, Cheng H, Yao L, Zhou H, Zhen J, Hu Z, Zhu W, Zhang F, Li X, Ren F, Chen L. Development of a multiple convolutional neural network-facilitated diagnostic screening program for immunofluorescence images of IgA nephropathy and idiopathic membranous nephropathy. Clin Kidney J 2023; 16:2503-2513. [PMID: 38046020 PMCID: PMC10689194 DOI: 10.1093/ckj/sfad153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Indexed: 12/05/2023] Open
Abstract
Background Immunoglobulin A nephropathy (IgAN) and idiopathic membranous nephropathy (IMN) are the most common glomerular diseases. Immunofluorescence (IF) tests of renal tissues are crucial for the diagnosis. We developed a multiple convolutional neural network (CNN)-facilitated diagnostic program to assist the IF diagnosis of IgAN and IMN. Methods The diagnostic program consisted of four parts: a CNN trained as a glomeruli detection module, an IF intensity comparator, dual-CNN (D-CNN) trained as a deposition appearance and location classifier and a post-processing module. A total of 1573 glomerular IF images from 1009 patients with glomerular diseases were used for the training and validation of the diagnostic program. A total of 1610 images of 426 patients from different hospitals were used as test datasets. The performance of the diagnostic program was compared with nephropathologists. Results In >90% of the tested images, the glomerulus location module achieved an intersection over union >0.8. The accuracy of the D-CNN in recognizing irregular granular mesangial deposition and fine granular deposition along the glomerular basement membrane was 96.1% and 93.3%, respectively. As for the diagnostic program, the accuracy, sensitivity and specificity of diagnosing suspected IgAN were 97.6%, 94.4% and 96.0%, respectively. The accuracy, sensitivity and specificity of diagnosing suspected IMN were 91.7%, 88.9% and 95.8%, respectively. The corresponding areas under the curve (AUCs) were 0.983 and 0.935. When tested with images from the outside hospital, the diagnostic program showed stable performance. The AUCs for diagnosing suspected IgAN and IMN were 0.972 and 0.948, respectively. Compared with inexperienced nephropathologists, the program showed better performance. Conclusion The proposed diagnostic program could assist the IF diagnosis of IgAN and IMN.
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Affiliation(s)
- Peng Xia
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhilong Lv
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yubing Wen
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | - Xuesong Zhao
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Boyao Zhang
- Beijing Zhijian Life Technology, Beijing, China
| | - Ying Wang
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haoyuan Cui
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chuanpeng Wang
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hua Zheng
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Qin
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijun Sun
- Department of Nephrology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Nan Ye
- Department of Nephrology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Hong Cheng
- Department of Nephrology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Li Yao
- Department of Nephrology, First Hospital Affiliated to China Medical University, Shenyang, China
| | - Hua Zhou
- Department of Nephrology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Junhui Zhen
- Department of Pathology, Qilu Hospital of Shandong University, Jinan, China
| | - Zhao Hu
- Department of Nephrology, Qilu Hospital of Shandong University, Jinan, China
| | - Weiguo Zhu
- Department of Information, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fa Zhang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Xuemei Li
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Ren
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Limeng Chen
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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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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Farris AB, Alexander MP, Balis UGJ, Barisoni L, Boor P, Bülow RD, Cornell LD, Demetris AJ, Farkash E, Hermsen M, Hogan J, Kain R, Kers J, Kong J, Levenson RM, Loupy A, Naesens M, Sarder P, Tomaszewski JE, van der Laak J, van Midden D, Yagi Y, Solez K. Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments. Transpl Int 2023; 36:11783. [PMID: 37908675 PMCID: PMC10614670 DOI: 10.3389/ti.2023.11783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/22/2023] [Indexed: 11/02/2023]
Abstract
The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.
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Affiliation(s)
- Alton B. Farris
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GE, United States
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ulysses G. J. Balis
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Laura Barisoni
- Department of Pathology and Medicine, Duke University, Durham, NC, United States
| | - Peter Boor
- Institute of Pathology, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University Clinic, Aachen, Germany
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Roman D. Bülow
- Institute of Pathology, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University Clinic, Aachen, Germany
| | - Lynn D. Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Anthony J. Demetris
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Evan Farkash
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Julien Hogan
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GE, United States
- Nephrology Service, Robert Debré Hospital, University of Paris, Paris, France
| | - Renate Kain
- Department of Pathology, Medical University of Vienna, Vienna, Austria
| | - Jesper Kers
- Department of Pathology, Amsterdam University Medical Centers, Amsterdam, Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
| | - Jun Kong
- Georgia State University, Atlanta, GA, United States
- Emory University, Atlanta, GA, United States
| | - Richard M. Levenson
- Department of Pathology, University of California Davis Health System, Sacramento, CA, United States
| | - Alexandre Loupy
- Institut National de la Santé et de la Recherche Médicale, UMR 970, Paris Translational Research Centre for Organ Transplantation, and Kidney Transplant Department, Hôpital Necker, Assistance Publique-Hôpitaux de Paris, University of Paris, Paris, France
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Pinaki Sarder
- Division of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, Intelligent Critical Care Center, College of Medicine, University of Florida at Gainesville, Gainesville, FL, United States
| | - John E. Tomaszewski
- Department of Pathology, The State University of New York at Buffalo, Buffalo, NY, United States
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Dominique van Midden
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Yukako Yagi
- Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kim Solez
- Department of Pathology, University of Alberta, Edmonton, AB, Canada
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Besusparis J, Morkunas M, Laurinavicius A. A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images. J Imaging 2023; 9:220. [PMID: 37888327 PMCID: PMC10607091 DOI: 10.3390/jimaging9100220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/26/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. MATERIALS AND METHODS We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. RESULTS A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers. CONCLUSIONS We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.
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Affiliation(s)
- Justinas Besusparis
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| | - Mindaugas Morkunas
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
| | - Arvydas Laurinavicius
- Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, LT-03101 Vilnius, Lithuania; (M.M.); (A.L.)
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, P. Baublio 5, LT-08406 Vilnius, Lithuania
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Yang Y, Sun K, Gao Y, Wang K, Yu G. Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance. Diagnostics (Basel) 2023; 13:3115. [PMID: 37835858 PMCID: PMC10572440 DOI: 10.3390/diagnostics13193115] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
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Affiliation(s)
- Yuanqing Yang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Department of Biomedical Engineering, School of Medical, Tsinghua University, Beijing 100084, China
| | - Kai Sun
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Furong Laboratory, Changsha 410013, China
| | - Yanhua Gao
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an 710068, China;
| | - Kuansong Wang
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410013, China;
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410013, China
| | - Gang Yu
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
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Layton AT. "Hi, how can i help you?": embracing artificial intelligence in kidney research. Am J Physiol Renal Physiol 2023; 325:F395-F406. [PMID: 37589052 DOI: 10.1152/ajprenal.00177.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 08/18/2023] Open
Abstract
In recent years, biology and precision medicine have benefited from major advancements in generating large-scale molecular and biomedical datasets and in analyzing those data using advanced machine learning algorithms. Machine learning applications in kidney physiology and pathophysiology include segmenting kidney structures from imaging data and predicting conditions like acute kidney injury or chronic kidney disease using electronic health records. Despite the potential of machine learning to revolutionize nephrology by providing innovative diagnostic and therapeutic tools, its adoption in kidney research has been slower than in other organ systems. Several factors contribute to this underutilization. The complexity of the kidney as an organ, with intricate physiology and specialized cell populations, makes it challenging to extrapolate bulk omics data to specific processes. In addition, kidney diseases often present with overlapping manifestations and morphological changes, making diagnosis and treatment complex. Moreover, kidney diseases receive less funding compared with other pathologies, leading to lower awareness and limited public-private partnerships. To promote the use of machine learning in kidney research, this review provides an introduction to machine learning and reviews its notable applications in renal research, such as morphological analysis, omics data examination, and disease diagnosis and prognosis. Challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the kidney research community to embrace machine learning as a powerful tool that can drive advancements in understanding kidney diseases and improving patient care.
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Affiliation(s)
- Anita T Layton
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
- Department of Biology, University of Waterloo, Waterloo, Ontario, Canada
- Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
- School of Pharmacology, University of Waterloo, Waterloo, Ontario, Canada
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Mubarak M, Raza A, Rashid R, Shakeel S. Evolution of human kidney allograft pathology diagnostics through 30 years of the Banff classification process. World J Transplant 2023; 13:221-238. [PMID: 37746037 PMCID: PMC10514746 DOI: 10.5500/wjt.v13.i5.221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 09/15/2023] Open
Abstract
The second half of the previous century witnessed a tremendous rise in the number of clinical kidney transplants worldwide. This activity was, however, accompanied by many issues and challenges. An accurate diagnosis and appropriate management of causes of graft dysfunction were and still are, a big challenge. Kidney allograft biopsy played a vital role in addressing the above challenge. However, its interpretation was not standardized for many years until, in 1991, the Banff process was started to fill this void. Thereafter, regular Banff meetings took place every 2 years for the past 30 years. Marked changes have taken place in the interpretation of kidney allograft biopsies, diagnosis, and classification of rejection and other non-rejection pathologies from the original Banff 93 classification. This review attempts to summarize those changes for increasing the awareness and understanding of kidney allograft pathology through the eyes of the Banff process. It will interest the transplant surgeons, physicians, pathologists, and allied professionals associated with the care of kidney transplant patients.
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Affiliation(s)
- Muhammed Mubarak
- Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
| | - Amber Raza
- Department of Nephrology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
| | - Rahma Rashid
- Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
| | - Shaheera Shakeel
- Department of Histopathology, Sindh Institute of Urology and Transplantation, Karachi 74200, Sindh, Pakistan
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Luchian A, Cepeda KT, Harwood R, Murray P, Wilm B, Kenny S, Pregel P, Ressel L. Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology. Biol Open 2023; 12:bio059988. [PMID: 37642317 PMCID: PMC10537956 DOI: 10.1242/bio.059988] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
This study focuses on ischaemia-reperfusion injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a convolutional neural network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular casts and Tubular necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman's rank correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.
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Affiliation(s)
- Andreea Luchian
- Department of Veterinary Anatomy Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, CH64 7TE, UK
| | - Katherine Trivino Cepeda
- Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7BE, UK
- Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7TX, UK
| | - Rachel Harwood
- Department of Paediatric Surgery, Alder Hey in the Park, Liverpool, L14 5AB, UK
| | - Patricia Murray
- Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7BE, UK
- Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7TX, UK
| | - Bettina Wilm
- Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7BE, UK
- Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7TX, UK
| | - Simon Kenny
- Department of Paediatric Surgery, Alder Hey in the Park, Liverpool, L14 5AB, UK
| | - Paola Pregel
- Department of Veterinary Sciences, University of Turin, Turin, 8-10124, Italy
| | - Lorenzo Ressel
- Department of Veterinary Anatomy Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, CH64 7TE, UK
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Rahman MA, Yilmaz I, Albadri ST, Salem FE, Dangott BJ, Taner CB, Nassar A, Akkus Z. Artificial Intelligence Advances in Transplant Pathology. Bioengineering (Basel) 2023; 10:1041. [PMID: 37760142 PMCID: PMC10525684 DOI: 10.3390/bioengineering10091041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/15/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.
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Affiliation(s)
- Md Arafatur Rahman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA
| | - Ibrahim Yilmaz
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Sam T. Albadri
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Fadi E. Salem
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Bryan J. Dangott
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
| | - C. Burcin Taner
- Department of Transplantation Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aziza Nassar
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Zeynettin Akkus
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224, USA
- Computational Pathology and Artificial Intelligence, Mayo Clinic, Jacksonville, FL 32224, USA
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Liu Q, Jiang N, Hao Y, Hao C, Wang W, Bian T, Wang X, Li H, zhang Y, Kang Y, Xie F, Li Y, Jiang X, Feng Y, Mao Z, Wang Q, Gao Q, Zhang W, Cui B, Dong T. Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images. Cancer Med 2023; 12:17952-17966. [PMID: 37559500 PMCID: PMC10523985 DOI: 10.1002/cam4.6437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM. METHODS A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. RESULTS In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. CONCLUSION DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.
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Affiliation(s)
- Qingqing Liu
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Nan Jiang
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Yiping Hao
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Chunyan Hao
- Department of Pathology, School of Basic Medical Science, Cheeloo College of MedicineShandong UniversityJinan CityChina
- Department of PathologyQilu Hospital of Shandong UniversityJinan CityChina
| | - Wei Wang
- Department of PathologyAffiliated Hospital of Jining Medical UniversityJining CityChina
| | - Tingting Bian
- Department of Medical ImagingAffiliated Hospital of Jining Medical UniversityJining CityChina
| | - Xiaohong Wang
- Department of Obstetrics and GynecologyJinan People's HospitalJinan CityChina
| | - Hua Li
- Department of Obstetrics and GynecologyTai'an City Central HospitalTai'an CityChina
| | - Yan zhang
- Department of Obstetrics and GynecologyWeifang People's HospitalWeifang CityChina
| | - Yanjun Kang
- Department of Obstetrics and GynecologyWomen and Children's Hospital, Qingdao UniversityQingdao CityChina
| | - Fengxiang Xie
- Department of PathologyKingMed DiagnosticsJinan CityChina
| | - Yawen Li
- Department of PathologyQilu Hospital of Shandong UniversityJinan CityChina
| | - XuJi Jiang
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Yuan Feng
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Zhonghao Mao
- Cheeloo College of MedicineShandong UniversityJinan CityChina
| | - Qi Wang
- Department of Obstetrics and Gynecology, Shandong Provincial Qianfoshan HospitalShandong UniversityJinan CityChina
| | - Qun Gao
- Department of Obstetrics and GynecologyThe Affiliated Hospital of Qingdao UniversityQingdao CityChina
| | - Wenjing Zhang
- Department of Obstetrics and GynecologyQilu Hospital of Shandong UniversityJinan CityChina
| | - Baoxia Cui
- Department of Obstetrics and GynecologyQilu Hospital of Shandong UniversityJinan CityChina
| | - Taotao Dong
- Department of Obstetrics and GynecologyQilu Hospital of Shandong UniversityJinan CityChina
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Cooper M, Ji Z, Krishnan RG. Machine learning in computational histopathology: Challenges and opportunities. Genes Chromosomes Cancer 2023; 62:540-556. [PMID: 37314068 DOI: 10.1002/gcc.23177] [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: 01/31/2023] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 06/15/2023] Open
Abstract
Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
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Affiliation(s)
- Michael Cooper
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Zongliang Ji
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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Akinrinmade AO, Adebile TM, Ezuma-Ebong C, Bolaji K, Ajufo A, Adigun AO, Mohammad M, Dike JC, Okobi OE. Artificial Intelligence in Healthcare: Perception and Reality. Cureus 2023; 15:e45594. [PMID: 37868407 PMCID: PMC10587915 DOI: 10.7759/cureus.45594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/24/2023] Open
Abstract
Artificial intelligence (AI) has birthed the new "big thing" in modern medicine. It promises to bring about safer and improved care that will be beneficial to patients and become a helpful tool in the hands of a skilled physician. Despite its anticipation, however, the implementation and usage of AI are still in their elementary phases, particularly due to legal and ethical considerations that border on "data." These challenges should not be brushed aside but rather be recognized and resolved to enable acceptance by all relevant stakeholders without prejudice. Once these challenges can be overcome, AI will truly revolutionize the field of medicine with improved diagnostic accuracy, a reduction in physician burnout, and an enhanced treatment modality. It is therefore paramount that AI be embraced by physicians and integrated into medical education in order to be well-prepared for our role in the future of medicine.
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Affiliation(s)
- Abidemi O Akinrinmade
- Medicine and Surgery, Benjamin S. Carson School of Medicine, Babcock University, Ilishan-Remo, NGA
| | - Temitayo M Adebile
- Public Health, Georgia Southern University, Statesboro, USA
- Nephrology, Boston Medical Center, Malden, USA
| | | | | | | | - Aisha O Adigun
- Infectious Diseases, University of Louisville, Louisville, USA
| | - Majed Mohammad
- Geriatrics, Mount Carmel Grove City Hospital, Grove City, USA
| | - Juliet C Dike
- Internal Medicine, University of Calabar, Calabar, NGA
| | - Okelue E Okobi
- Family Medicine, Larkin Community Hospital Palm Springs Campus, Miami, USA
- Family Medicine, Medficient Health Systems, Laurel, USA
- Family Medicine, Lakeside Medical Center, Belle Glade, USA
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Deng R, Liu Q, Cui C, Yao T, Long J, Asad Z, Womick RM, Zhu Z, Fogo AB, Zhao S, Yang H, Huo Y. Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation. IEEE Trans Biomed Eng 2023; 70:2636-2644. [PMID: 37030838 PMCID: PMC10517077 DOI: 10.1109/tbme.2023.3260739] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this article, we propose the Omni-Seg network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5× to 40× scale) pathological image segmentation via a single neural network. The contribution of this article is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from 150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics).
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Denic A, Gaddam M, Moustafa A, Mullan AF, Luehrs AC, Sharma V, Thompson RH, Smith ML, Alexander MP, Lerman LO, Barisoni L, Rule AD. Tubular and Glomerular Size by Cortex Depth as Predictor of Progressive CKD after Radical Nephrectomy for Tumor. J Am Soc Nephrol 2023; 34:1535-1545. [PMID: 37430426 PMCID: PMC10482069 DOI: 10.1681/asn.0000000000000180] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/08/2023] [Indexed: 07/12/2023] Open
Abstract
SIGNIFICANCE STATEMENT Glomerular size differs by cortex depth. Larger nephrons are prognostic of progressive kidney disease, but it is unknown whether this risk differs by cortex depth or by glomeruli versus proximal or distal tubule size. We studied the average minor axis diameter in oval proximal and distal tubules separately and by cortex depth in patients who had radical nephrectomy to remove a tumor from 2019 to 2020. In adjusted analyses, larger glomerular volume in the middle and deep cortex predicted progressive kidney disease. Wider proximal tubular diameter did not predict progressive kidney disease independent of glomerular volume. Wider distal tubular diameter showed a gradient of strength of prediction of progressive kidney disease in the more superficial cortex than in the deep cortex. BACKGROUND Larger nephrons are prognostic of progressive kidney disease, but whether this risk differs by nephron segments or by depth in the cortex is unclear. METHODS We studied patients who underwent radical nephrectomy for a tumor between 2000 and 2019. Large wedge kidney sections were scanned into digital images. We estimated the diameters of proximal and distal tubules by the minor axis of oval tubular profiles and estimated glomerular volume with the Weibel-Gomez stereological model. Analyses were performed separately in the superficial, middle, and deep cortex. Cox proportional hazard models assessed the risk of progressive CKD (dialysis, kidney transplantation, sustained eGFR <10 ml/min per 1.73 m 2 , or a sustained 40% decline from the postnephrectomy baseline eGFR) with glomerular volume or tubule diameters. At each cortical depth, models were unadjusted, adjusted for glomerular volume or tubular diameter, and further adjusted for clinical characteristics (age, sex, body mass index, hypertension, diabetes, postnephrectomy baseline eGFR, and proteinuria). RESULTS Among 1367 patients were 62 progressive CKD events during a median follow-up of 4.5 years. Glomerular volume predicted CKD outcomes at all depths, but only in the middle and deep cortex after adjusted analyses. Proximal tubular diameter also predicted progressive CKD at any depth but not after adjusted analyses. Distal tubular diameter showed a gradient of more strongly predicting progressive CKD in the superficial than deep cortex, even in adjusted analysis. CONCLUSIONS Larger glomeruli are independent predictors of progressive CKD in the deeper cortex, whereas in the superficial cortex, wider distal tubular diameters are an independent predictor of progressive CKD.
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Affiliation(s)
- Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Mrunanjali Gaddam
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Amr Moustafa
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Aidan F. Mullan
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Anthony C. Luehrs
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Vidit Sharma
- Department of Urology, Mayo Clinic, Rochester, Minnesota
| | | | - Maxwell L. Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Lilach O. Lerman
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Laura Barisoni
- Department of Pathology and Medicine, Duke University, Durham, North Carolina
| | - Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
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Timakova A, Ananev V, Fayzullin A, Makarov V, Ivanova E, Shekhter A, Timashev P. Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology. Biomolecules 2023; 13:1327. [PMID: 37759727 PMCID: PMC10526383 DOI: 10.3390/biom13091327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/23/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.
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Affiliation(s)
- Anna Timakova
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
| | - Vladislav Ananev
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, 173003 Veliky Novgorod, Russia; (V.A.); (V.M.)
| | - Alexey Fayzullin
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
| | - Vladimir Makarov
- Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, 173003 Veliky Novgorod, Russia; (V.A.); (V.M.)
| | - Elena Ivanova
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
- B.V. Petrovsky Russian Research Center of Surgery, 2 Abrikosovskiy Lane, 119991 Moscow, Russia
| | - Anatoly Shekhter
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
| | - Peter Timashev
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia; (A.T.); (A.F.); (E.I.); (P.T.)
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya St., 119991 Moscow, Russia
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50
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Caliò A, Barreca A, Marletta S, Achenza MIS, Alessi M, Angelico R, Apicella L, Argiolas D, Bossini N, Carrano R, Carriero C, Castellano G, Comai G, Di Bella C, D’Ignoto F, Gallico A, Gastaldon F, Merlotti G, Paloschi V, Panarese A, Parodi A, Perna F, Picciotto D, Regalia A, Rossini M, Russo E, Salerno MP, Toti L, Tulissi P, Vischini G, Zaza G, Eccher A. Histology for nephrology, from pre-implantation to post-transplant kidney biopsy. Lesson learned from ReBIrth (Renal BIopsy for Kidney Transplantation Therapy). Pathologica 2023; 115:199-204. [PMID: 37314869 PMCID: PMC10688244 DOI: 10.32074/1591-951x-858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
A meeting entitled Renal BIopsy for Kidney Transplantation Therapy (ReBIrth) took place on May 31st, 2022 in Bologna, Italy. The meeting drew together nephrologists, surgeons, and pathologists and recognized as experts in the field of kidney transplantation in Italy. In this paper, we present our experience working with kidney transplants in the current era of immunosuppression therapy. The primary aim is to report the histopathological characteristics of failed kidney allografts after a consensus of experts reviewed the cases on a wholeslide imaging digital platform. Regardless of the cases discussed, digital pathology was reliable in identifying all the morphological and immunohistochemical features required to improve the correct use of immunosuppressive therapy to prevent graft failure and optimize patient management.
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Affiliation(s)
- Anna Caliò
- Department of Diagnostic and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
| | - Antonella Barreca
- Pathology Unit, “Città della Salute e della Scienza di Torino” University Hospital, Turin, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
- Department of Pathology, Pederzoli Hospital, Peschiera del Garda, Italy
| | | | - Marianna Alessi
- Department of Medicine, Nephrology, University of Padova, Padova, Italy
| | - Roberta Angelico
- Department of Surgical Sciences, HPB and Transplant Unit, University of Rome Tor Vergata, Roma Italy
| | - Luca Apicella
- Division of Nephrology, Dialysis and Renal Transplantation, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, Salerno, Italy
| | - Davide Argiolas
- Renal Transplant Unit, Azienda Ospedaliera Brotzu, Cagliari, Italy
| | - Nicola Bossini
- Division of Nephrology and Dialysis, ASST Spedali Civili, Brescia, Italy
| | - Rosa Carrano
- UOSD Nefrologia e Trapianto Renale, Dipartimento di Chirurgia Generale e Chirurgie Specialistiche dei Trapianti di Rene, Nefrologia, Cure Intensive e del Dolore, Azienda Ospedaliera Universitaria Federico II, Napoli, Italy
| | | | - Giuseppe Castellano
- Nephrology, Dialysis and Transplantation, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Giorgia Comai
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Caterina Di Bella
- Kidney and Pancreas Transplantation Unit, Department of Surgical, Oncological and Gastroenterological Sciences, University of Padua, Padua, Italy
| | | | - Agnese Gallico
- Division of Nephrology and Dialysis, ASST Spedali Civili, Brescia, Italy
| | - Fiorella Gastaldon
- Unità Operativa Complessa di Nefrologia, Dialisi e Trapianto Renale, AULSS8 Berica, Ospedale San Bortolo, Vicenza, Italy
| | - Guido Merlotti
- Nephrology and Kidney Transplantation Unit, Department of Translational Medicine, University of Piemonte Orientale (UPO), “Maggiore della Carità” University Hospital, Novara, Italy
| | - Vera Paloschi
- Transplant Medicine Unit, Department of Internal Medicine, IRCCS San Raffaele Hospital, Milano, Italy
| | - Alessandra Panarese
- Department of Biotechnological and Applied Clinical Sciences (DISCAB), University of L’Aquila, L’Aquila, Italy
| | - Angelica Parodi
- Unit of Nephrology Dialysis and Transplantation, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Francesco Perna
- Fondazione Policlinico A. Gemelli – UOS Trapianti di Rene, Roma, Italy
| | - Daniela Picciotto
- Unit of Nephrology Dialysis and Transplantation, Department of Internal Medicine, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Anna Regalia
- Nephrology, Dialysis and Transplantation, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Michele Rossini
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Enrico Russo
- General Surgery and Kidney Transplantation Unit, “OO.RR. San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy
| | | | - Luca Toti
- Transplant and HPB Unit, Department of Surgical Sciences, University of Rome Tor Vergata, Rome, Italy
| | - Patrizia Tulissi
- Unità complessa di Nefrologia, Dialisi e Trapianto, Udine, Italy
| | - Gisella Vischini
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Gianluigi Zaza
- Nephrology, Dialysis and Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Albino Eccher
- Department of Diagnostic and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy
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