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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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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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Kondelaji MHR, Sharma GP, Jagtap J, Shafiee S, Hansen C, Gasperetti T, Frei A, Veley D, Narayanan J, Fish BL, Parchur AK, Ibrahim ESH, Medhora M, Himburg HA, Joshi A. 2 nd Window NIR Imaging of Radiation Injury Mitigation Provided by Reduced Notch-Dll4 Expression on Vasculature. Mol Imaging Biol 2024; 26:124-137. [PMID: 37530966 PMCID: PMC11188939 DOI: 10.1007/s11307-023-01840-7] [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: 05/03/2023] [Revised: 07/07/2023] [Accepted: 07/12/2023] [Indexed: 08/03/2023]
Abstract
PURPOSE Vascular endothelium plays a central role in the pathogenesis of acute and chronic radiation injuries, yet the mechanisms which promote sustained endothelial dysfunction and contribute to late responding organ failure are unclear. We employed 2nd window (> 1100 nm emission) Near-Infrared (NIR) imaging using indocyanine green (ICG) to track and define the role of the notch ligand Delta-like ligand 4 (Dll4) in mediating vascular injury in two late-responding radiosensitive organs: the lung and kidney. PROCEDURES Consomic strains of female Salt Sensitive or SS (Dll4-high) and SS with 3rd chromosome inherited from Brown Norway, SS.BN3 (Dll4-low) rats at ages 11-12 weeks were used to demonstrate the impact of reduced Dll4 expression on long-term vascular integrity, renal function, and survival following high-dose 13 Gy partial body irradiation at 42- and 90 days post-radiation. 2nd window dynamic NIR fluorescence imaging with ICG was analyzed with physiology-based pharmacokinetic modeling and confirmed with assays of endothelial Dll4 expression to assess the role of endogenous Dll4 expression on radiation injury protection. RESULTS We show that SS.BN3 (Dll4-low) rats are relatively protected from vascular permeability disruption compared to the SS (Dll4-high) strain. We further demonstrated that SS.BN3 (Dll4-low) rats have reduced radiation induced loss of CD31+ vascular endothelial cells, and increased Dll4 vascular expression is correlated with vascular dysfunction. CONCLUSIONS Together, these data suggest Dll4 plays a key role in pathogenesis of radiation-induced vascular injury to the lung and kidney.
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Affiliation(s)
| | - Guru Prasad Sharma
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jaidip Jagtap
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Shayan Shafiee
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Christopher Hansen
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Tracy Gasperetti
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anne Frei
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dana Veley
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jayashree Narayanan
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Brian L Fish
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Abdul K Parchur
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - El-Sayed H Ibrahim
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Meetha Medhora
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Heather A Himburg
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Amit Joshi
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, 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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5
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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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Liu X, Wu Y, Chen Y, Hui D, Zhang J, Hao F, Lu Y, Cheng H, Zeng Y, Han W, Wang C, Li M, Zhou X, Zheng W. Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators. Comput Biol Med 2023; 166:107470. [PMID: 37722173 DOI: 10.1016/j.compbiomed.2023.107470] [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: 06/15/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 09/20/2023]
Abstract
Diagnosis of diabetic kidney disease (DKD) mainly relies on screening the morphological variations and internal lesions of glomeruli from pathological kidney biopsy. The prominent pathological alterations of glomeruli for DKD include glomerular hypertrophy and nodular mesangial sclerosis. However, the qualitative judgment of these alterations is inaccurate and inconstant due to the intra- and inter-subject variability of pathologists. It is necessary to design artificial intelligence (AI) methods for accurate quantification of these pathological alterations and outcome prediction of DKD. In this work, we present an AI-driven framework to quantify the volume of glomeruli and degree of nodular mesangial sclerosis, respectively, based on an instance segmentation module and a novel weakly supervised Macro-Micro Aggregation (MMA) module. Subsequently, we construct classic machine learning models to predict the degree of DKD based on three selected pathological indicators via factor analysis. These corresponding modules are trained and tested on a total of 281 whole slide images (WSIs) digitized from two hospitals with different scanners. Our designed AI framework achieved inspiring results with 0.926 mIoU for glomerulus segmentation, and 0.899 F1 score for glomerulus classification in the external testing dataset. Meantime, the visualized results of the MMA module could reflect the location of the lesions. The performance of predicting disease achieved the F1 score of 0.917, which further proved the effectiveness of our AI-driven quantification of pathological indicators. Additionally, the interpretation of the machine learning model with the SHAP method showed similar accordance with the development of DKD in pathology. In conclusion, the proposed auxiliary diagnostic technologies have the feasibility for quantitative analysis of glomerular pathological tissues and alterations in DKD. Pathological quantitative indicators will also make it more convenient to provide doctors with assistance in clinical practice.
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Affiliation(s)
- Xueyu Liu
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yongfei Wu
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China.
| | - Yilin Chen
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Dongna Hui
- Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Jianan Zhang
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Fang Hao
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yuanyue Lu
- Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
| | - Hangbei Cheng
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Yue Zeng
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Weixia Han
- Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Chen Wang
- Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Ming Li
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
| | - Xiaoshuang Zhou
- Department of Nephrology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
| | - Wen Zheng
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China
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7
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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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8
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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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9
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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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10
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Al-Thelaya K, Gilal NU, Alzubaidi M, Majeed F, Agus M, Schneider J, Househ M. Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey. J Pathol Inform 2023; 14:100335. [PMID: 37928897 PMCID: PMC10622844 DOI: 10.1016/j.jpi.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 11/07/2023] Open
Abstract
Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.
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Affiliation(s)
- Khaled Al-Thelaya
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Nauman Ullah Gilal
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mahmood Alzubaidi
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Fahad Majeed
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Marco Agus
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Jens Schneider
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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11
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Hwang JH, Lim M, Han G, Park H, Kim YB, Park J, Jun SY, Lee J, Cho JW. A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies. Toxicol Res 2023; 39:399-408. [PMID: 37398569 PMCID: PMC10313597 DOI: 10.1007/s43188-023-00173-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 07/04/2023] Open
Abstract
Deep learning has recently become one of the most popular methods of image analysis. In non-clinical studies, several tissue slides are generated to investigate the toxicity of a test compound. These are converted into digital image data using a slide scanner, which is then studied by researchers to investigate abnormalities, and the deep learning method has been started to adopt in this study. However, comparative studies evaluating different deep learning algorithms for analyzing abnormal lesions are scarce. In this study, we applied three algorithms, SSD, Mask R-CNN, and DeepLabV3+, to detect hepatic necrosis in slide images and determine the best deep learning algorithm for analyzing abnormal lesions. We trained each algorithm on 5750 images and 5835 annotations of hepatic necrosis including validation and test, augmented with 500 image tiles of 448 × 448 pixels. Precision, recall, and accuracy were calculated for each algorithm based on the prediction results of 60 test images of 2688 × 2688 pixels. The two segmentation algorithms, DeepLabV3+ and Mask R-CNN, showed over 90% of accuracy (0.94 and 0.92, respectively), whereas SSD, an object detection algorithm, showed lower accuracy. The trained DeepLabV3+ outperformed all others in recall while also successfully separating hepatic necrosis from other features in the test images. It is important to localize and separate the abnormal lesion of interest from other features to investigate it on a slide level. Therefore, we suggest that segmentation algorithms are more appropriate than object detection algorithms for use in the pathological analysis of images in non-clinical studies. Supplementary Information The online version contains supplementary material available at 10.1007/s43188-023-00173-5.
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Affiliation(s)
- Ji-Hee Hwang
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Minyoung Lim
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Gyeongjin Han
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Heejin Park
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Yong-Bum Kim
- Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
| | - Jinseok Park
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Sang-Yeop Jun
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Jaeku Lee
- Research & Development Team, LAC Inc, Seoul, 07807 Republic of Korea
| | - Jae-Woo Cho
- Toxicologic Pathology Research Group, Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114 Republic of Korea
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12
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Tyshynsky R, Sensarma S, Riedl M, Bukowy J, Schramm LP, Vulchanova L, Osborn JW. Periglomerular afferent innervation of the mouse renal cortex. Front Neurosci 2023; 17:974197. [PMID: 36777644 PMCID: PMC9909228 DOI: 10.3389/fnins.2023.974197] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Recent studies using a novel method for targeted ablation of afferent renal nerves have demonstrated their importance in the development and maintenance of some animal models of hypertension. However, relatively little is known about the anatomy of renal afferent nerves distal to the renal pelvis. Here, we investigated the anatomical relationship between renal glomeruli and afferent axons identified based on transient receptor potential vanilloid 1 channel (TRPV1) lineage or calcitonin gene related peptide (CGRP) immunolabeling. Analysis of over 6,000 (10,000 was accurate prior to the removal of the TH data during the review process) glomeruli from wildtype C57BL/6J mice and transgenic mice expressing tdTomato in TRPV1 lineage cells indicated that approximately half of all glomeruli sampled were closely apposed to tdTomato+ or CGRP+ afferent axons. Glomeruli were categorized as superficial, midcortical, or juxtamedullary based on their depth within the cortex. Juxtamedullary glomeruli were more likely to be closely apposed by afferent axon subtypes than more superficial glomeruli. High-resolution imaging of thick, cleared renal slices and subsequent distance transformations revealed that CGRP+ axons closely apposed to glomeruli were often found within 2 microns of nephrin+ labeling of glomerular podocytes. Furthermore, imaging of thick slices suggested that CGRP+ axon bundles can closely appose multiple glomeruli that share the same interlobular artery. Based on their expression of CGRP or tdTomato, prevalence near glomeruli, proximity to glomerular structures, and close apposition to multiple glomeruli within a module, we hypothesize that periglomerular afferent axons may function as mechanoreceptors monitoring glomerular pressure. These anatomical findings highlight the importance of further studies investigating the physiological role of periglomerular afferent axons in neural control of renal function in health and disease.
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Affiliation(s)
- Roman Tyshynsky
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - Sulagna Sensarma
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States
| | - Maureen Riedl
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, Milwaukee, WI, United States
| | - Lawrence P. Schramm
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Lucy Vulchanova
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, United States,Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - John W. Osborn
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, United States,Department of Surgery, University of Minnesota, Minneapolis, MN, United States,*Correspondence: John W. Osborn,
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13
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Labriffe M, Woillard JB, Gwinner W, Braesen JH, Anglicheau D, Rabant M, Koshy P, Naesens M, Marquet P. Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data. Am J Transplant 2022; 22:2821-2833. [PMID: 36062389 DOI: 10.1111/ajt.17192] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 01/25/2023]
Abstract
Interpretation of kidney graft biopsies using the Banff classification is still heterogeneous. In this study, extreme gradient boosting classifiers learned from two large training datasets (n = 631 and 304 cases) where the "reference diagnoses" were not strictly defined following the Banff rules but from central reading by expert pathologists and further interpreted consensually by experienced transplant nephrologists, in light of the clinical context. In three external validation datasets (n = 3744, 589, and 360), the classifiers yielded a mean ROC curve AUC (95%CI) of: 0.97 (0.92-1.00), 0.97 (0.96-0.97), and 0.95 (0.93-0.97) for antibody-mediated rejection (ABMR); 0.94 (0.91-0.96), 0.94 (0.92-0.95), and 0.91 (0.88-0.95) for T cell-mediated rejection; >0.96 (0.90-1.00) with all three for interstitial fibrosis-tubular atrophy. We also developed a classifier to discriminate active and chronic active ABMR with 95% accuracy. In conclusion, we built highly sensitive and specific artificial intelligence classifiers able to interpret kidney graft scoring together with a few clinical data and automatically diagnose rejection, with excellent concordance with the Banff rules and reference diagnoses made by a group of experts. Some discrepancies may point toward possible improvements that could be made to the Banff classification.
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Affiliation(s)
- Marc Labriffe
- Pharmacology & Transplantation, INSERM U1248, Université de Limoges, Limoges, France.,Department of Pharmacology, Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France
| | - Jean-Baptiste Woillard
- Pharmacology & Transplantation, INSERM U1248, Université de Limoges, Limoges, France.,Department of Pharmacology, Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France
| | - Wilfried Gwinner
- Nephrology, Internal Medicine, Hannover Medical School, Hannover, Germany
| | - Jan-Hinrich Braesen
- Institute for Pathology, Nephropathology Unit, Hannover Medical School, Germany
| | - Dany Anglicheau
- Université de Paris, Paris, France.,INSERM U1151, Paris, France.,Department of Nephrology and Kidney Transplantation, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Marion Rabant
- Department of Pathology, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Priyanka Koshy
- Department of Pathology, University Hospitals Leuven, Leuven, Belgium
| | - Maarten Naesens
- Nephrology and Renal Transplantation Research Group, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.,Department of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium
| | - Pierre Marquet
- Pharmacology & Transplantation, INSERM U1248, Université de Limoges, Limoges, France.,Department of Pharmacology, Toxicology and Pharmacovigilance, CHU de Limoges, Limoges, France
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14
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Hermsen M, Ciompi F, Adefidipe A, Denic A, Dendooven A, Smith BH, van Midden D, Bräsen JH, Kers J, Stegall MD, Bándi P, Nguyen T, Swiderska-Chadaj Z, Smeets B, Hilbrands LB, van der Laak JAWM. Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:1418-1432. [PMID: 35843265 DOI: 10.1016/j.ajpath.2022.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 06/13/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3+ cell density within scarred regions and higher CD3+ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.
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Affiliation(s)
- Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Adeyemi Adefidipe
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Amélie Dendooven
- Department of Pathology, Ghent University Hospital, Ghent, Belgium; Faculty of Medicine, University of Antwerp, Wilrijk, Antwerp, Belgium
| | - Byron H Smith
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Dominique van Midden
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jan Hinrich Bräsen
- Nephropathology Unit, Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Jesper Kers
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands; Center for Analytical Sciences Amsterdam, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Mark D Stegall
- Division of Transplantation Surgery, Mayo Clinic, Rochester, Minnesota
| | - Péter Bándi
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tri Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Zaneta Swiderska-Chadaj
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands; Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
| | - Bart Smeets
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Luuk B Hilbrands
- Department of Nephrology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen A W M van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
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15
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Yao T, Lu Y, Long J, Jha A, Zhu Z, Asad Z, Yang H, Fogo AB, Huo Y. Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining. J Med Imaging (Bellingham) 2022; 9:052408. [PMID: 35747553 DOI: 10.1117/1.jmi.9.5.052408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. Approach: The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified-GSS (associated with hypertension-related injury), disappearing-GSS (a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent-GSS (nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. Results: The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice dimilarity coefficient. Conclusion: We develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is user-friendly to non-technical users via a single line of command. The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/Glo-In-One.
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Affiliation(s)
- Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Yuzhe Lu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Jun Long
- Central South University, Big Data Institute, Changsha, China
| | - Aadarsh Jha
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Zheyu Zhu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Zuhayr Asad
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Haichun Yang
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, Tennessee, United States
| | - Agnes B Fogo
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
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16
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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17
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Patel AU, Shaker N, Mohanty S, Sharma S, Gangal S, Eloy C, Parwani AV. Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence. Diagnostics (Basel) 2022; 12:diagnostics12081778. [PMID: 35892487 PMCID: PMC9332710 DOI: 10.3390/diagnostics12081778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.
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Affiliation(s)
- Ankush U. Patel
- Mayo Clinic Department of Laboratory Medicine and Pathology, Rochester, MN 55905, USA
- Correspondence: ; Tel.: +1-206-451-3519
| | - Nada Shaker
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
| | - Sambit Mohanty
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
- Advanced Medical Research Institute, Bareilly 243001, India
| | - Shivani Sharma
- CORE Diagnostics, Gurugram 122016, India; (S.M.); (S.S.)
| | - Shivam Gangal
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- College of Engineering, Biomedical Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Catarina Eloy
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Rua Júlio Amaral de Carvalho, 45, 4200-135 Porto, Portugal;
- Institute for Research and Innovation in Health (I3S Consortium), Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
| | - Anil V. Parwani
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA; (N.S.); (S.G.); (A.V.P.)
- Cooperative Human Tissue Network (CHTN) Midwestern Division, Columbus, OH 43240, USA
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18
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Natural Language Processing in Diagnostic Texts from Nephropathology. Diagnostics (Basel) 2022; 12:diagnostics12071726. [PMID: 35885630 PMCID: PMC9325286 DOI: 10.3390/diagnostics12071726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction: This study investigates whether it is possible to predict a final diagnosis based on a written nephropathological description—as a surrogate for image analysis—using various NLP methods. Methods: For this work, 1107 unlabelled nephropathological reports were included. (i) First, after separating each report into its microscopic description and diagnosis section, the diagnosis sections were clustered unsupervised to less than 20 diagnostic groups using different clustering techniques. (ii) Second, different text classification methods were used to predict the diagnostic group based on the microscopic description section. Results: The best clustering results (i) could be achieved with HDBSCAN, using BoW-based feature extraction methods. Based on keywords, these clusters can be mapped to certain diagnostic groups. A transformer encoder-based approach as well as an SVM worked best regarding diagnosis prediction based on the histomorphological description (ii). Certain diagnosis groups reached F1-scores of up to 0.892 while others achieved weak classification metrics. Conclusion: While textual morphological description alone enables retrieving the correct diagnosis for some entities, it does not work sufficiently for other entities. This is in accordance with a previous image analysis study on glomerular change patterns, where some diagnoses are associated with one pattern, but for others, there exists a complex pattern combination.
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19
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Rinschen MM, Palygin O, El-Meanawy A, Domingo-Almenara X, Palermo A, Dissanayake LV, Golosova D, Schafroth MA, Guijas C, Demir F, Jaegers J, Gliozzi ML, Xue J, Hoehne M, Benzing T, Kok BP, Saez E, Bleich M, Himmerkus N, Weisz OA, Cravatt BF, Krüger M, Benton HP, Siuzdak G, Staruschenko A. Accelerated lysine metabolism conveys kidney protection in salt-sensitive hypertension. Nat Commun 2022; 13:4099. [PMID: 35835746 PMCID: PMC9283537 DOI: 10.1038/s41467-022-31670-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 06/27/2022] [Indexed: 01/07/2023] Open
Abstract
Hypertension and kidney disease have been repeatedly associated with genomic variants and alterations of lysine metabolism. Here, we combined stable isotope labeling with untargeted metabolomics to investigate lysine's metabolic fate in vivo. Dietary 13C6 labeled lysine was tracked to lysine metabolites across various organs. Globally, lysine reacts rapidly with molecules of the central carbon metabolism, but incorporates slowly into proteins and acylcarnitines. Lysine metabolism is accelerated in a rat model of hypertension and kidney damage, chiefly through N-alpha-mediated degradation. Lysine administration diminished development of hypertension and kidney injury. Protective mechanisms include diuresis, further acceleration of lysine conjugate formation, and inhibition of tubular albumin uptake. Lysine also conjugates with malonyl-CoA to form a novel metabolite Nε-malonyl-lysine to deplete malonyl-CoA from fatty acid synthesis. Through conjugate formation and excretion as fructoselysine, saccharopine, and Nε-acetyllysine, lysine lead to depletion of central carbon metabolites from the organism and kidney. Consistently, lysine administration to patients at risk for hypertension and kidney disease inhibited tubular albumin uptake, increased lysine conjugate formation, and reduced tricarboxylic acid (TCA) cycle metabolites, compared to kidney-healthy volunteers. In conclusion, lysine isotope tracing mapped an accelerated metabolism in hypertension, and lysine administration could protect kidneys in hypertensive kidney disease.
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Affiliation(s)
- Markus M Rinschen
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA.
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- III. Medical Clinic, University Hospital Hamburg Eppendorf, Hamburg, Germany.
- AIAS, Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Aarhus, Denmark.
| | - Oleg Palygin
- Division of Nephrology, Department of Medicine, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Ashraf El-Meanawy
- Division of Nephrology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xavier Domingo-Almenara
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA
- Omics Sciences Unit, EURECAT, Technology Centre of Catalonia, Reus, Catalonia, Spain
| | - Amelia Palermo
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, USA
| | - Lashodya V Dissanayake
- Department of Molecular Pharmacology and Physiology, University of South Florida, Tampa, FL, 33602, USA
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Daria Golosova
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | | | - Carlos Guijas
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA
| | - Fatih Demir
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | | | - Megan L Gliozzi
- Renal Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
| | - Jingchuan Xue
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA
| | - Martin Hoehne
- Center for Molecular Medicine Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, Cologne, Germany
- Department II of Internal Medicine, University Hospital of Cologne, Cologne, Germany
| | - Thomas Benzing
- Center for Molecular Medicine Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, Cologne, Germany
- Department II of Internal Medicine, University Hospital of Cologne, Cologne, Germany
| | - Bernard P Kok
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, 92037, USA
| | - Enrique Saez
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, 92037, USA
| | - Markus Bleich
- Institute of Physiology, University Kiel, Kiel, Germany
| | | | - Ora A Weisz
- Renal Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
| | | | - Marcus Krüger
- Center for Molecular Medicine Cologne, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, Cologne, Germany
| | - H Paul Benton
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA
| | - Gary Siuzdak
- Scripps Center for Metabolomics, Scripps Research, La Jolla, CA, 92037, USA.
| | - Alexander Staruschenko
- Department of Molecular Pharmacology and Physiology, University of South Florida, Tampa, FL, 33602, USA.
- Department of Physiology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
- James A. Haley Veterans' Hospital, Tampa, FL, 33612, USA.
- Hypertension and Kidney Research Center, University of South Florida, Tampa, FL, 33602, USA.
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20
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Hara S, Haneda E, Kawakami M, Morita K, Nishioka R, Zoshima T, Kometani M, Yoneda T, Kawano M, Karashima S, Nambo H. Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules. PLoS One 2022; 17:e0271161. [PMID: 35816495 PMCID: PMC9273082 DOI: 10.1371/journal.pone.0271161] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 06/27/2022] [Indexed: 12/02/2022] Open
Abstract
Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.
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Affiliation(s)
- Satoshi Hara
- Medical Education Research Center, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Emi Haneda
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Masaki Kawakami
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Kento Morita
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
| | - Ryo Nishioka
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Takeshi Zoshima
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Mitsuhiro Kometani
- Department of Endocrinology and Metabolism, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
| | - Takashi Yoneda
- Department of Endocrinology and Metabolism, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
- Department of Health Promotion and Medicine of the Future, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
- Faculty of Transdisciplinary Sciences, Institute of Transdisciplinary Sciences, Kanazawa University, Kanazawa, Japan
| | - Mitsuhiro Kawano
- Department of Rheumatology, Kanazawa University Graduate School of Medicine, Kanazawa, Japan
- * E-mail: (MK); (HN)
| | | | - Hidetaka Nambo
- School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan
- * E-mail: (MK); (HN)
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21
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Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images. MATHEMATICS 2022. [DOI: 10.3390/math10111934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.
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22
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Hodgin JB, Mariani LH, Zee J, Liu Q, Smith AR, Eddy S, Hartman J, Hamidi H, Gaut JP, Palmer MB, Nast CC, Chang A, Hewitt S, Gillespie BW, Kretzler M, Holzman LB, Barisoni L. Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome. Am J Kidney Dis 2022; 79:807-819.e1. [PMID: 34864148 DOI: 10.1053/j.ajkd.2021.10.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022]
Abstract
RATIONALE & OBJECTIVE The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies nor the clinical and molecular heterogeneity of these diseases. STUDY DESIGN Prospective observational cohort study. SETTING & PARTICIPANTS 221 MCD and FSGS patients enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). EXPOSURE The NEPTUNE Digital Pathology Scoring System (NDPSS) was applied to generate scores for 37 glomerular descriptors. OUTCOME Time from biopsy to complete proteinuria remission, time from biopsy to kidney disease progression (40% estimated glomerular filtration rate [eGFR] decline or kidney failure), and eGFR over time. ANALYTICAL APPROACH Cluster analysis was used to group patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for associations with outcomes using adjusted Cox models and linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes between clusters and identify genes associated with individual descriptors driving cluster membership. RESULTS Three clusters were identified: X (n = 56), Y (n = 68), and Z (n = 97). Clusters Y and Z had higher probabilities of proteinuria remission (HRs of 1.95 [95% CI, 0.99-3.85] and 3.29 [95% CI, 1.52-7.13], respectively), lower hazards of disease progression (HRs of 0.22 [95% CI, 0.08-0.57] and 0.11 [95% CI, 0.03-0.45], respectively), and lower loss of eGFR over time compared with X. Cluster X had 1,920 genes that were differentially expressed compared with Y+Z; these reflected activation of pathways of immune response and inflammation. Six descriptors driving the clusters individually correlated with clinical outcomes and gene expression. LIMITATIONS Low prevalence of some descriptors and biopsy at a single time point. CONCLUSIONS The NDPSS allows for categorization of FSGS/MCD patients into clinically and biologically relevant subgroups, and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems.
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Affiliation(s)
- Jeffrey B Hodgin
- Renal Pathology, Department of Pathology, University of Michigan, Ann Arbor, Michigan.
| | - Laura H Mariani
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jarcy Zee
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Qian Liu
- Arbor Research Collaborative for Health, Ann Arbor, Michigan, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail R Smith
- Arbor Research Collaborative for Health, Ann Arbor, Michigan, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sean Eddy
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - John Hartman
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Habib Hamidi
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Joseph P Gaut
- Department of Pathology and Immunology, and Internal Medicine, Washington University, St. Louis, Missouri
| | - Matthew B Palmer
- Department of Pathology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cynthia C Nast
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Anthony Chang
- Department of Pathology, University of Chicago Medicine, Chicago, Illinois
| | - Stephen Hewitt
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Brenda W Gillespie
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan
| | - Matthias Kretzler
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Lawrence B Holzman
- Renal-Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Laura Barisoni
- Department of Pathology, Division of AI & Computational Pathology, Duke University, Durham, North Carolina; Department of Medicine, Division of Nephrology, Duke University, Durham, North Carolina.
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23
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Gupta L, Klinkhammer BM, Seikrit C, Fan N, Bouteldja N, Gräbel P, Gadermayr M, Boor P, Merhof D. Large-scale extraction of interpretable features provides new insights into kidney histopathology – a proof-of-concept study. J Pathol Inform 2022; 13:100097. [PMID: 36268111 PMCID: PMC9576990 DOI: 10.1016/j.jpi.2022.100097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/14/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
Whole slide images contain a magnitude of quantitative information that may not be fully explored in qualitative visual assessments. We propose: (1) a novel pipeline for extracting a comprehensive set of visual features, which are detectable by a pathologist, as well as sub-visual features, which are not discernible by human experts and (2) perform detailed analyses on renal images from mice with experimental unilateral ureteral obstruction. An important criterion for these features is that they are easy to interpret, as opposed to features obtained from neural networks. We extract and compare features from pathological and healthy control kidneys to learn how the compartments (glomerulus, Bowman's capsule, tubule, interstitium, artery, and arterial lumen) are affected by the pathology. We define feature selection methods to extract the most informative and discriminative features. We perform statistical analyses to understand the relation of the extracted features, both individually, and in combinations, with tissue morphology and pathology. Particularly for the presented case-study, we highlight features that are affected in each compartment. With this, prior biological knowledge, such as the increase in interstitial nuclei, is confirmed and presented in a quantitative way, alongside with novel findings, like color and intensity changes in glomeruli and Bowman's capsule. The proposed approach is therefore an important step towards quantitative, reproducible, and rater-independent analysis in histopathology.
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Affiliation(s)
- Laxmi Gupta
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
- Corresponding author.
| | | | - Claudia Seikrit
- Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Nina Fan
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Nassim Bouteldja
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
- Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Philipp Gräbel
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Michael Gadermayr
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
- Salzburg University of Applied Sciences, Puch/Salzburg, Austria
| | - Peter Boor
- Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
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24
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Girolami I, Pantanowitz L, Marletta S, Hermsen M, van der Laak J, Munari E, Furian L, Vistoli F, Zaza G, Cardillo M, Gesualdo L, Gambaro G, Eccher A. Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review. J Nephrol 2022; 35:1801-1808. [PMID: 35441256 PMCID: PMC9458558 DOI: 10.1007/s40620-022-01327-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/28/2022] [Indexed: 10/29/2022]
Abstract
BACKGROUND Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy. METHODS A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study. RESULTS Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising. CONCLUSION All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice.
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Affiliation(s)
- Ilaria Girolami
- Division of Pathology, Central Hospital Bolzano, Bolzano, Italy
| | - Liron Pantanowitz
- Department of Pathology and Clinical Labs, University of Michigan, Ann Arbor, MI, USA
| | - Stefano Marletta
- Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy
| | - Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Enrico Munari
- Pathology Unit, Department of Molecular and Translational Medicine, Spedali Civili-University of Brescia, Brescia, Italy
| | - Lucrezia Furian
- Department of Surgical, Oncological and Gastroenterological Sciences, Unit of Kidney and Pancreas Transplantation, University of Padua, Padua, Italy
| | - Fabio Vistoli
- Division of General and Transplant Surgery, University of Pisa, Pisa, Italy
| | - Gianluigi Zaza
- Department of Nephro-Urology, Nephrology, Dialysis and Transplant Unit, University of Foggia, Foggia, Italy
| | | | - Loreto Gesualdo
- Nephrology, Dialysis, and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Giovanni Gambaro
- Department of General Medicine, Renal Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy.
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25
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Identification of glomerulosclerosis using IBM Watson and shallow neural networks. J Nephrol 2022; 35:1235-1242. [PMID: 35041197 PMCID: PMC8765108 DOI: 10.1007/s40620-021-01200-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 11/03/2021] [Indexed: 11/26/2022]
Abstract
Background Advanced stages of different renal diseases feature glomerular sclerosis at a histological level which is observed by light microscopy on tissue samples obtained by performing a kidney biopsy. Computer-aided diagnosis (CAD) systems leverage the potential of artificial intelligence (AI) in healthcare to support physicians in the diagnostic process. Methods We propose a novel CAD system that processes histological images and discriminates between sclerotic and non-sclerotic glomeruli. To this goal, we designed, tested, and compared two artificial neural network (ANN) classifiers. The former implements a shallow ANN classifying hand-crafted features extracted from Regions of Interest (ROIs) by means of image-processing procedures. The latter, instead, employs the IBM Watson Visual Recognition System, which uses a deep artificial neural network making decisions taking the images as input, without the need to design any procedure for describing images with features. The input dataset consisted of 428 sclerotic glomeruli and 2344 non-sclerotic glomeruli derived from images of kidney biopsies scanned by the Aperio ScanScope System. Results Both AI approaches allowed to very accurately distinguish (mean MCC 0.95 and mean Accuracy 0.99) between sclerotic and non-sclerotic glomeruli. Although the systems may seem interchangeable, the approach based on feature extraction and classification would allow clinicians to gain information on the most discriminating features. In fact, further procedures could explain the classifier’s decision by analysing which subset of features impacted the most on the final decision. Conclusions We developed a customizable support system that can facilitate the work of renal pathologists both in clinical and research settings. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s40620-021-01200-0.
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26
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Weis CA, Bindzus JN, Voigt J, Runz M, Hertjens S, Gaida MM, Popovic ZV, Porubsky S. Assessment of glomerular morphological patterns by deep learning algorithms. J Nephrol 2022; 35:417-427. [PMID: 34982414 PMCID: PMC8927010 DOI: 10.1007/s40620-021-01221-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2021] [Indexed: 12/11/2022]
Abstract
Background Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology. Methods For this purpose, we defined nine classes of glomerular morphological patterns and trained twelve convolutional neuronal network (CNN) models on these. The two-step training process was done on a first dataset defined by an expert nephropathologist (12,253 images) and a second consensus dataset (11,142 images) defined by three experts in the field. Results The efficacy of CNN training was evaluated using another set with 180 consensus images, showing convincingly good classification results (kappa-values 0.838–0.938). Furthermore, we elucidated the image areas decisive for CNN-based decision making by class activation maps. Finally, we demonstrated that the algorithm could decipher glomerular disease patterns coinciding in a single glomerulus (e.g. necrosis along with mesangial and endocapillary hypercellularity). Conclusions In summary, our model, focusing on glomerular lesions detectable by conventional microscopy, is the first sui generis to deploy deep learning as a reliable and promising tool in recognition of even discrete and/or overlapping morphological changes. Our results provide a stimulus for ongoing projects that integrate further input levels next to morphology (such as immunohistochemistry, electron microscopy, and clinical information) to develop a novel tool applicable for routine diagnostic nephropathology. Supplementary Information The online version contains supplementary material available at 10.1007/s40620-021-01221-9.
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Affiliation(s)
- Cleo-Aron Weis
- Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.
| | - Jan Niklas Bindzus
- Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany
| | - Jonas Voigt
- Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany
| | - Marlen Runz
- Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.,Mannheim Institute for Intelligent Systems in Medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Svetlana Hertjens
- Institute of Medical Statistics and Biometry, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Matthias M Gaida
- Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany
| | - Zoran V Popovic
- Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany
| | - Stefan Porubsky
- Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.
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Noriaki S, Eiichiro U, Yasushi O. Artificial Intelligence in Kidney Pathology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Gowrishankar S, Gupta K, Maitra D. Whole slide imaging vs eyeballing: The future in quantification of tubular atrophy in routine clinical practice. Indian J Nephrol 2022; 32:151-155. [PMID: 35603119 PMCID: PMC9121712 DOI: 10.4103/ijn.ijn_333_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 09/10/2020] [Accepted: 10/18/2020] [Indexed: 11/04/2022] Open
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Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021; 12:42. [PMID: 34881097 PMCID: PMC8609289 DOI: 10.4103/jpi.jpi_36_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open
Abstract
Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
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Affiliation(s)
- Shima Mehrvar
- Preclinical Safety, AbbVie Inc., North Chicago, IL, USA
| | | | - Pradeep Babburi
- Business Technology Solutions, AbbVie Inc., North Chicago, IL, USA
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30
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Arthurs C, Roufosse C. Forging the tools for a computer-aided workflow in transplant pathology. Lancet Digit Health 2021; 4:e2-e3. [PMID: 34794931 DOI: 10.1016/s2589-7500(21)00254-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/29/2021] [Indexed: 10/19/2022]
Affiliation(s)
- Callum Arthurs
- Centre for Inflammatory Diseases, Department of Immunology and Inflammation, Imperial College London, London, UK; Department of Cellular Pathology, North West London Pathology, London, UK.
| | - Candice Roufosse
- Centre for Inflammatory Diseases, Department of Immunology and Inflammation, Imperial College London, London, UK; Department of Cellular Pathology, North West London Pathology, London, UK
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31
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Li X, Davis RC, Xu Y, Wang Z, Souma N, Sotolongo G, Bell J, Ellis M, Howell D, Shen X, Lafata KJ, Barisoni L. Deep learning segmentation of glomeruli on kidney donor frozen sections. J Med Imaging (Bellingham) 2021; 8:067501. [PMID: 34950750 PMCID: PMC8685284 DOI: 10.1117/1.jmi.8.6.067501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/08/2021] [Indexed: 10/15/2023] Open
Abstract
Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution ( n = 123 ) and at external institutions ( n = 135 ) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (ratio: 0.8:0.2), and external WSIs were used as an independent testing dataset. Nonsclerotic ( n = 22767 ) and sclerotic ( n = 1366 ) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic and sclerotic glomeruli. DL-derived, manual segmentation, and reported glomerular count (standard of care) were compared. Results: The average Dice similarity coefficient testing was 0.90 and 0.83. And the F 1 , recall, and precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for nonsclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation-derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.
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Affiliation(s)
- Xiang Li
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
| | - Richard C. Davis
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Yuemei Xu
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
- Nanjing Drum Tower Hospital, Department of Pathology, Nanjing, China
| | - Zehan Wang
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Nao Souma
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
| | - Gina Sotolongo
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Jonathan Bell
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Matthew Ellis
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
- Duke University, Department of Surgery, Durham, North Carolina, United States
| | - David Howell
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Xiling Shen
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Kyle J. Lafata
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Laura Barisoni
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
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Cascarano GD, Debitonto FS, Lemma R, Brunetti A, Buongiorno D, De Feudis I, Guerriero A, Venere U, Matino S, Rocchetti MT, Rossini M, Pesce F, Gesualdo L, Bevilacqua V. A neural network for glomerulus classification based on histological images of kidney biopsy. BMC Med Inform Decis Mak 2021; 21:300. [PMID: 34724926 PMCID: PMC8559346 DOI: 10.1186/s12911-021-01650-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/06/2021] [Indexed: 11/26/2022] Open
Abstract
Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results.
CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes.
We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.
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Affiliation(s)
- Giacomo Donato Cascarano
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | | | - Ruggero Lemma
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | - Irio De Feudis
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy.,Apulian Bioengineering s.r.l., Modugno, BA, Italy
| | - Andrea Guerriero
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy
| | - Umberto Venere
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Silvia Matino
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Maria Teresa Rocchetti
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Michele Rossini
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Francesco Pesce
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Loreto Gesualdo
- Department of Emergency and Organ Transplantation, Nephrology Unit University of Bari Aldo Moro, Bari, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bary, Italy. .,Apulian Bioengineering s.r.l., Modugno, BA, Italy.
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Yi Z, Salem F, Menon MC, Keung K, Xi C, Hultin S, Haroon Al Rasheed MR, Li L, Su F, Sun Z, Wei C, Huang W, Fredericks S, Lin Q, Banu K, Wong G, Rogers NM, Farouk S, Cravedi P, Shingde M, Smith RN, Rosales IA, O'Connell PJ, Colvin RB, Murphy B, Zhang W. Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies. Kidney Int 2021; 101:288-298. [PMID: 34757124 DOI: 10.1016/j.kint.2021.09.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/12/2021] [Accepted: 09/09/2021] [Indexed: 10/19/2022]
Abstract
Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte infiltrates. Periodic acid- Schiff stained slides of transplant biopsies (60 training and 33 testing) were used to quantify pathological lesions specific for interstitium, tubules and mononuclear leukocyte infiltration. The pipeline was applied to the whole slide images from 789 transplant biopsies (478 baseline [pre-implantation] and 311 post-transplant 12-month protocol biopsies) in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. Our model accurately recognized kidney tissue compartments and mononuclear leukocytes. The digital features significantly correlated with revised Banff 2007 scores but were more sensitive to subtle pathological changes below the thresholds in the Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of one-year graft loss, while a Composite Damage Score in 12-month post-transplant protocol biopsies predicted later graft loss. ITASs and Composite Damage Scores outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITASs or Composite Damage Scores also demonstrated significantly higher incidence of estimated glomerular filtration rate decline and subsequent graft damage. Thus, our deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies and demonstrated superior ability for prediction of post-transplant graft loss with potential application as a prevention, risk stratification or monitoring tool.
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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
| | - Fadi Salem
- Pathology Division, Department of Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Madhav C Menon
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Nephrology Division, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Karen Keung
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia; Department of Nephrology, Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Caixia Xi
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sebastian Hultin
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - M Rizwan Haroon Al Rasheed
- Pathology Division, Department of Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Li Li
- Pathology Division, Department of Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fei Su
- Renal Division, Department of 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
| | - Chengguo Wei
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Weiqing Huang
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Samuel Fredericks
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Qisheng Lin
- Nephrology Division, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Khadija Banu
- Nephrology Division, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Germaine Wong
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Natasha M Rogers
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Samira Farouk
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Paolo Cravedi
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Meena Shingde
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - R Neal Smith
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ivy A Rosales
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Philip J O'Connell
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia; Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Department of Nephrology, Westmead Hospital, Sydney, New South Wales, Australia
| | - Robert B Colvin
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Barbara Murphy
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Weijia Zhang
- Renal Division, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Liu X, Li M, Wu Y, Chen Y, Hao F, Zhou D, Wang C, Ma C, Shi G, Zhou X. An efficient glomerular object locator for renal whole slide images using proposal-free network and dynamic scale evaluation method. AI COMMUN 2021. [DOI: 10.3233/aic-210073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In the diagnosis of chronic kidney disease, glomerulus as the blood filter provides important information for an accurate disease diagnosis. Thus automatic localization of the glomeruli is the necessary groundwork for future auxiliary kidney disease diagnosis, such as glomerular classification and area measurement. In this paper, we propose an efficient glomerular object locator in kidney whole slide image(WSI) based on proposal-free network and dynamic scale evaluation method. In the training phase, we construct an intensive proposal-free network which can learn efficiently the fine-grained features of the glomerulus. In the evaluation phase, a dynamic scale evaluation method is utilized to help the well-trained model find the most appropriate evaluation scale for each high-resolution WSI. We collect and digitalize 1204 renal biopsy microscope slides containing more than 41000 annotated glomeruli, which is the largest number of dataset to our best knowledge. We validate the each component of the proposed locator via the ablation study. Experimental results confirm that the proposed locator outperforms recently proposed approaches and pathologists by comparing F 1 and run time in localizing glomeruli from WSIs at a resolution of 0.25 μm/pixel and thus achieves state-of-the-art performance. Particularly, the proposed locator can be embedded into the renal intelligent auxiliary diagnosis system for renal clinical diagnosis by localizing glomeruli in high-resolution WSIs effectively.
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Affiliation(s)
- Xueyu Liu
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Ming Li
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Yongfei Wu
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
- Faculty of Science and Technology, University of Macau, Taipa, Macau, China. E-mail:
| | - Yilin Chen
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Fang Hao
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Daoxiang Zhou
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Chen Wang
- Department of Pathology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China. E-mail:
| | - Chuanfeng Ma
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Guangze Shi
- College of Data Science, Taiyuan University of Technology, Taiyuan, Shanxi, China. E-mail:
| | - Xiaoshuang Zhou
- Department of Nephrology, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China. E-mail:
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35
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Jiang L, Chen W, Dong B, Mei K, Zhu C, Liu J, Cai M, Yan Y, Wang G, Zuo L, Shi H. A Deep Learning-Based Approach for Glomeruli Instance Segmentation from Multistained Renal Biopsy Pathologic Images. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1431-1441. [PMID: 34294192 DOI: 10.1016/j.ajpath.2021.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 04/02/2021] [Accepted: 05/07/2021] [Indexed: 12/20/2022]
Abstract
Glomeruli instance segmentation from pathologic images is a fundamental step in the automatic analysis of renal biopsies. Glomerular histologic manifestations vary widely among diseases and cases, and several special staining methods are necessary for pathologic diagnosis. A robust model is needed to segment and classify glomeruli with different staining methods and apply in cases with various glomerular pathologic changes. Herein, pathologic images from renal biopsy slides stained with three basic special staining methods were used to build the data sets. The snapshot group included 1970 glomeruli from 516 patients, and the whole-slide image group included 8665 glomeruli from 148 patients. Cascade Mask region-based convolutional neural net architecture was trained to detect, classify, and segment glomeruli into three categories: i) GN, structural normal; ii) global sclerosis; and iii) glomerular with other lesions. In the snapshot group, total glomeruli, GN, global sclerosis, and glomerular with other lesions achieved an F1 score of 0.914, 0.896, 0.681, and 0.756, respectively, which were comparable with those in the whole-slide image group (0.940, 0.839, 0.806, and 0.753, respectively). Among the three categories, GN achieved the best instance segmentation effect in both groups, as determined by average precision, average recall, F1 score, and Mask mean Intersection over Union. The present model segments and classifies multistained glomeruli with efficiency and robustness. It can be applied as the first step for more detailed glomerular histologic analysis.
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Affiliation(s)
- Lei Jiang
- Electron Microscope Lab, Peking University People's Hospital, Beijing, China
| | - Wenkai Chen
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Bao Dong
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Ke Mei
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Chuang Zhu
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Jun Liu
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Meishun Cai
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Yu Yan
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Gongwei Wang
- Department of Pathology, Peking University People's Hospital, Beijing, China
| | - Li Zuo
- Department of Nephrology, Peking University People's Hospital, Beijing, China
| | - Hongxia Shi
- Electron Microscope Lab, Peking University People's Hospital, Beijing, China.
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36
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Deng R, Yang H, Jha A, Lu Y, Chu P, Fogo AB, Huo Y. Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole Slide Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1924-1933. [PMID: 33780334 PMCID: PMC8249345 DOI: 10.1109/tmi.2021.3069154] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
There has been a long pursuit for precise and reproducible glomerular quantification on renal pathology to leverage both research and practice. When digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of serial sections from the same tissue can be acquired as a stack of images, similar to frames in a video. In radiology, the stack of images (e.g., computed tomography) are naturally used to provide 3D context for organs, tissues, and tumors. In pathology, it is appealing to do a similar 3D assessment. However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI. In this paper, we propose a novel Multi-object Association for Pathology in 3D (Map3D) method for automatically identifying and associating large-scale cross-sections of 3D objects from routine serial sectioning and WSI. The innovations of the Multi-Object Association for Pathology in 3D (Map3D) method are three-fold: (1) the large-scale glomerular association is formed as a new multi-object tracking (MOT) perspective; (2) the quality-aware whole series registration is proposed to not only provide affinity estimation but also offer automatic kidney-wise quality assurance (QA) for registration; (3) a dual-path association method is proposed to tackle the large deformation, missing tissues, and artifacts during tracking. To the best of our knowledge, the Map3D method is the first approach that enables automatic and large-scale glomerular association across 3D serial sectioning using WSI. Our proposed method Map3D achieved MOTA = 44.6, which is 12.1% higher than the non-deep learning benchmarks.
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37
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Farris AB, Vizcarra J, Amgad M, Donald Cooper LA, Gutman D, Hogan J. Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification. Kidney Int Rep 2021; 6:1878-1887. [PMID: 34307982 PMCID: PMC8258455 DOI: 10.1016/j.ekir.2021.04.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 03/28/2021] [Accepted: 04/12/2021] [Indexed: 10/31/2022] Open
Abstract
INTRODUCTION Digital pathology improves the standardization and reproducibility of kidney biopsy specimen assessment. We developed a pipeline allowing the analysis of many images without requiring human preprocessing and illustrate its use with a simple algorithm for quantification of interstitial fibrosis on a large dataset of kidney allograft biopsy specimens. METHODS Masson trichrome-stained images from kidney allograft biopsy specimens were used to train and validate a glomeruli detection algorithm using a VGG19 convolutional neural network and an automatic cortical region of interest (ROI) selection algorithm including cortical regions containing all predicted glomeruli. A positive-pixel count algorithm was used to quantify interstitial fibrosis on the ROIs and the association between automatic fibrosis and pathologist evaluation, estimated glomerular filtration rate (GFR) and allograft survival was assessed. RESULTS The glomeruli detection (F1 score of 0.87) and ROIs selection (F1 score 0.83 [SD 0.13]) algorithms displayed high accuracy. The correlation between the automatic fibrosis quantification on manually and automatically selected ROIs was high (r = 1.00 [0.99-1.00]). Automatic fibrosis quantification was only moderately correlated with pathologists' assessment and was not significantly associated with eGFR or allograft survival. CONCLUSION This pipeline can automatically and accurately detect glomeruli and select cortical ROIs that can easily be used to develop, validate, and apply image analysis algorithms.
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Affiliation(s)
- Alton Brad Farris
- Department of Pathology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Juan Vizcarra
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Mohamed Amgad
- Center for Computational Imaging and Signal Analytics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Lee Alex Donald Cooper
- Center for Computational Imaging and Signal Analytics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - David Gutman
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Julien Hogan
- Emory Transplant Center, Department of Surgery, Emory University School of Medicine, Atlanta, Georgia, USA
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38
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Huo Y, Deng R, Liu Q, Fogo AB, Yang H. AI applications in renal pathology. Kidney Int 2021; 99:1309-1320. [PMID: 33581198 PMCID: PMC8154730 DOI: 10.1016/j.kint.2021.01.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 12/20/2022]
Abstract
The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.
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Affiliation(s)
- Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Ruining Deng
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Quan Liu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Haichun Yang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
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39
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Spires DR, Palygin O, Levchenko V, Isaeva E, Klemens CA, Khedr S, Nikolaienko O, Kriegel A, Cheng X, Yeo JY, Joe B, Staruschenko A. Sexual dimorphism in the progression of type 2 diabetic kidney disease in T2DN rats. Physiol Genomics 2021; 53:223-234. [PMID: 33870721 PMCID: PMC8285576 DOI: 10.1152/physiolgenomics.00009.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/05/2021] [Accepted: 04/14/2021] [Indexed: 12/14/2022] Open
Abstract
Diabetic kidney disease (DKD) is a common complication of diabetes, which frequently leads to end-stage renal failure and increases cardiovascular disease risk. Hyperglycemia promotes renal pathologies such as glomerulosclerosis, tubular hypertrophy, microalbuminuria, and a decline in glomerular filtration rate. Importantly, recent clinical data have demonstrated distinct sexual dimorphism in the pathogenesis of DKD in people with diabetes, which impacts both severity- and age-related risk factors. This study aimed to define sexual dimorphism and renal function in a nonobese type 2 diabetes model with the spontaneous development of advanced diabetic nephropathy (T2DN rats). T2DN rats at 12- and over 48-wk old were used to define disease progression and kidney injury development. We found impaired glucose tolerance and glomerular hyperfiltration in T2DN rats to compare with nondiabetic Wistar control. The T2DN rat displays a significant sexual dimorphism in insulin resistance, plasma cholesterol, renal and glomerular injury, urinary nephrin shedding, and albumin handling. Our results indicate that both male and female T2DN rats developed nonobese type 2 DKD phenotype, where the females had significant protection from the development of severe forms of DKD. Our findings provide further evidence for the T2DN rat strain's effectiveness for studying the multiple facets of DKD.
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Affiliation(s)
- Denisha R Spires
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Oleg Palygin
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Vladislav Levchenko
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elena Isaeva
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Christine A Klemens
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sherif Khedr
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
- Department of Physiology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Oksana Nikolaienko
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Alison Kriegel
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Xi Cheng
- Department of Physiology and Pharmacology, University of Toledo, Ohio
| | - Ji-Youn Yeo
- Department of Physiology and Pharmacology, University of Toledo, Ohio
| | - Bina Joe
- Department of Physiology and Pharmacology, University of Toledo, Ohio
| | - Alexander Staruschenko
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
- Cardiovascular Center, Medical College of Wisconsin, Milwaukee, Wisconsin
- Clement J. Zablocki Veterans Affairs Medical Center, Milwaukee, Wisconsin
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40
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Salvi M, Mogetta A, Gambella A, Molinaro L, Barreca A, Papotti M, Molinari F. Automated assessment of glomerulosclerosis and tubular atrophy using deep learning. Comput Med Imaging Graph 2021; 90:101930. [PMID: 33964790 DOI: 10.1016/j.compmedimag.2021.101930] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 02/20/2021] [Accepted: 04/21/2021] [Indexed: 01/16/2023]
Abstract
In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity.
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Affiliation(s)
- Massimo Salvi
- Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy.
| | - Alessandro Mogetta
- Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy
| | - Alessandro Gambella
- Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, Turin, 10126, Italy
| | - Luca Molinaro
- A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy
| | - Antonella Barreca
- A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy
| | - Mauro Papotti
- University of Turin, Division of Pathology, Department of Oncology, Via Santena 7, Turin, 10126, Italy
| | - Filippo Molinari
- Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy
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41
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Wilbur DC, Smith ML, Cornell LD, Andryushkin A, Pettus JR. Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross-institutional study. Histopathology 2021; 79:499-508. [PMID: 33813779 DOI: 10.1111/his.14376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/11/2021] [Accepted: 03/29/2021] [Indexed: 11/30/2022]
Abstract
AIMS Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on slides from multiple institutions. We used machine learning to identify glomeruli on renal biopsies and compared performance between single and multiple institutions. METHODS AND RESULTS Randomly selected, adequately sampled renal core biopsy cases (71) consisting of four stains each (haematoxylin and eosin, trichrome, silver, periodic acid Schiff) from three institutions were digitised at ×40. Glomeruli were manually annotated by three renal pathologists using a digital tool. Cases were divided into training/validation (n = 52) and evaluation (n = 19) cohorts. An algorithm was trained to develop three convolutional neural network (CNN) models which tested case cohorts intra- and inter-institutionally. Raw CNN search data from each of the four slides per case were merged into composite regions of interest containing putative glomeruli. The sensitivity and modified specificity of glomerulus detection (versus annotated truth) were calculated for each model/cohort. Intra-institutional (3) sensitivity ranged from 90 to 93%, with modified specificity from 86 to 98%. Interinstitutional (1) sensitivity was 77%, with modified specificity 97%. Combined intra- and inter-institutional (1) sensitivity was 86%, with modified specificity 92%. CONCLUSIONS Feature detection sensitivity degrades when training and test material originate from different sites. Training using a combined set of digital slides from three institutions improves performance. Differing histology methods probably account for algorithm performance contrasts. Our data highlight the need for diverse training sets for the development of generalisable machine learning histology algorithms.
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Affiliation(s)
| | - Maxwell L Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic - Scottsdale, Phoenix, AZ, USA
| | - Lynn D Cornell
- Department of Laboratory Medicine and Pathology, Mayo Clinic - Rochester, Rochester, MN, USA
| | | | - Jason R Pettus
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, MH, USA
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42
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Ginley B, Jen KY, Han SS, Rodrigues L, Jain S, Fogo AB, Zuckerman J, Walavalkar V, Miecznikowski JC, Wen Y, Yen F, Yun D, Moon KC, Rosenberg A, Parikh C, Sarder P. Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis. J Am Soc Nephrol 2021; 32:837-850. [PMID: 33622976 PMCID: PMC8017538 DOI: 10.1681/asn.2020050652] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 12/14/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.
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Affiliation(s)
- Brandon Ginley
- Departments of Pathology and Anatomical Sciences, University at Buffalo - The State University of New York, Buffalo, New York
| | - Kuang-Yu Jen
- Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, California
| | - Seung Seok Han
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Luís Rodrigues
- University Clinic of Nephrology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.,Nephrology Unit, Coimbra Hospital and University Center, Coimbra, Portugal
| | - Sanjay Jain
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Agnes B Fogo
- Departments of Pathology, Microbiology, and Immunology, and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Jonathan Zuckerman
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - Vighnesh Walavalkar
- Department of Pathology, University of California at San Francisco, San Francisco, California
| | - Jeffrey C Miecznikowski
- Department of Biostatistics, University at Buffalo - The State University of New York, Buffalo, New York
| | - Yumeng Wen
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Felicia Yen
- Department of Pathology and Laboratory Medicine, University of California at Davis, Sacramento, California
| | - Donghwan Yun
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Kyung Chul Moon
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Avi Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Chirag Parikh
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Pinaki Sarder
- Departments of Pathology and Anatomical Sciences, University at Buffalo - The State University of New York, Buffalo, New York.,Department of Biomedical Engineering, University at Buffalo - The State University of New York, Buffalo, New York
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43
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Girolami I, Marletta S, Eccher A. Commentary: The Digital Fate of Glomeruli in Renal Biopsy. J Pathol Inform 2021; 12:14. [PMID: 34012718 PMCID: PMC8112342 DOI: 10.4103/jpi.jpi_102_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 01/09/2021] [Accepted: 01/09/2021] [Indexed: 11/04/2022] Open
Affiliation(s)
- Ilaria Girolami
- Division of Pathology, Central Hospital Bolzano, Bolzano, Italy
| | - Stefano Marletta
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
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44
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Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology 2021; 78:791-804. [PMID: 33211332 DOI: 10.1111/his.14304] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.
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Affiliation(s)
- Alton B Farris
- Department of Pathology and Laboratory Medicine, Atlanta, GA, USA
| | - Juan Vizcarra
- Department of Bioinformatics, Emory University, Atlanta, GA, USA
| | - Mohamed Amgad
- Department of Pathology and Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA
| | - Lee A D Cooper
- Department of Pathology and Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA
| | - David Gutman
- Department of Bioinformatics, Emory University, Atlanta, GA, USA
| | - Julien Hogan
- Department of Surgery, Emory University, Atlanta, GA, USA
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45
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Hacking S, Bijol V. Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy. Ultrastruct Pathol 2021; 45:118-127. [PMID: 33583322 DOI: 10.1080/01913123.2021.1882628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer vision to a wider medical audience and deep learning on the cloud allows one to design, develop, train and deploy applications with ease. In the field of histopathology, the implementation of various applications in AI has been successful for whole slide images rich in biological diversity. However, the analysis of other tissue medias, including electron microscopy, is yet to be explored. The present study aims to evaluate deep learning for the classification of medical kidney disease on electron microscopy images: amyloidosis, diabetic glomerulosclerosis, membranous nephropathy, membranoproliferative glomerulonephritis (MPGN), and thin basement membrane disease (TBMD). We found good overall classification with the MedKidneyEM-v1 Classifier and when looking at normal and diseased kidneys, the average area under the curve for precision and recall was 0.841. The average area under the curve for precision and recall on the disease only cohort was 0.909. Digital pathology will shape a new era for medical kidney disease and the present study demonstrates the feasibility of deep learning for electron microscopy. Future approaches could be used by renal pathologists to improve diagnostic concordance, determine therapeutic strategies, and optimize patient outcomes in a true clinical environment.
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Affiliation(s)
- Sean Hacking
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Northwell, Manhasset, New York, USA
| | - Vanesa Bijol
- Department of Pathology and Laboratory Medicine, Donald and Barbara Zucker School of Medicine at Northwell, Manhasset, New York, USA
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Cicalese PA, Mobiny A, Shahmoradi Z, Yi X, Mohan C, Van Nguyen H. Kidney Level Lupus Nephritis Classification Using Uncertainty Guided Bayesian Convolutional Neural Networks. IEEE J Biomed Health Inform 2021; 25:315-324. [PMID: 33206612 DOI: 10.1109/jbhi.2020.3039162] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality. Although various Computer Aided Diagnosis (CAD) systems have been developed for other nephrohistopathological applications, little has been done to accurately classify kidneys based on their kidney level Lupus Glomerulonephritis (LGN) scores. The successful implementation of CAD systems has also been hindered by the diagnosing physician's perceived classifier strengths and weaknesses, which has been shown to have a negative effect on patient outcomes. We propose an Uncertainty-Guided Bayesian Classification (UGBC) scheme that is designed to accurately classify control, class I/II, and class III/IV LGN (3 class) at both the glomerular-level classification task (26,634 segmented glomerulus images) and the kidney-level classification task (87 MRL/lpr mouse kidney sections). Data annotation was performed using a high throughput, bulk labeling scheme that is designed to take advantage of Deep Neural Network's (or DNNs) resistance to label noise. Our augmented UGBC scheme achieved a 94.5% weighted glomerular-level accuracy while achieving a weighted kidney-level accuracy of 96.6%, improving upon the standard Convolutional Neural Network (CNN) architecture by 11.8% and 3.5% respectively.
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Abstract
PURPOSE OF REVIEW Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications. RECENT FINDINGS The primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge. SUMMARY Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.
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48
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A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues. Comput Med Imaging Graph 2021; 89:101865. [PMID: 33548823 DOI: 10.1016/j.compmedimag.2021.101865] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 12/01/2020] [Accepted: 12/28/2020] [Indexed: 01/22/2023]
Abstract
Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney and identify individuals requiring treatment. Because microscopic identification of sclerosed glomeruli performed under the microscope is labor intensive, we developed a deep learning (DL) approach to identify and classify glomeruli as normal or sclerosed in digital whole slide images (WSIs). The segmentation and classification of glomeruli was performed by the U-Net model. Subsequently, glomerular classifications were refined based on glomerular histomorphometry. The U-Net model was trained using patches from Periodic Acid-Schiff (PAS) stained WSIs (n=31) from the AIDPATH - a multi-center dataset, and then tested on an independent set of WSIs (n=20) including PAS (n=6), and hematoxylin and eosin (H&E) stained WSIs (n=14) from four other institutions. The training and test WSIs were obtained from formalin fixed and paraffin embedded blocks with of human kidney specimens each presenting various proportions of normal and sclerosed glomeruli. In the PAS stained WSIs, normal and sclerosed glomeruli were respectively classified with the F1-score of 97.5% and 68.8%. In the H&E stained WSIs, the F1-scores of 90.8% and 78.1% were achieved. Regardless the tissue staining, the glomeruli in the test WSIs were classified with the F1-score of 94.5% (n=923, normal) and 76.8% for (n=261, sclerosed). These results demonstrate for the first time that a framework based on the U-Net model trained with glomerular patches from PAS stained WSIs can reliably segment and classify normal and sclerosed glomeruli in PAS and also H&E stained WSIs. Our approach yielded higher accuracy of glomerular classifications than some of the recently published methods. Additionally, our test set of images with ground truth is publicly available.
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49
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Abstract
Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.
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50
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Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021; 69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/18/2020] [Accepted: 12/31/2020] [Indexed: 12/31/2022]
Abstract
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.
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Affiliation(s)
- Israa Alnazer
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France; AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Pascal Bourdon
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Thierry Urruty
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Omar Falou
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon; American University of Culture and Education, Koura, Lebanon; Lebanese University, Faculty of Science, Tripoli, Lebanon
| | - Mohamad Khalil
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Ahmad Shahin
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Christine Fernandez-Maloigne
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
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