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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: 31] [Impact Index Per Article: 10.3] [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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Marsh JN, Liu TC, Wilson PC, Swamidass SJ, Gaut JP. Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens. JAMA Netw Open 2021; 4:e2030939. [PMID: 33471115 PMCID: PMC7818108 DOI: 10.1001/jamanetworkopen.2020.30939] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
IMPORTANCE A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. OBJECTIVE To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. MAIN OUTCOMES AND MEASURES Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. RESULTS The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. CONCLUSIONS AND RELEVANCE The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.
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Affiliation(s)
- Jon N Marsh
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Institute for Informatics (I 2 ), Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ta-Chiang Liu
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Parker C Wilson
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Institute for Informatics (I 2 ), Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Joseph P Gaut
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Department of Medicine, Washington University School of Medicine in St Louis, St Louis, Missouri
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Yao L, Zhang H, Zhang M, Chen X, Zhang J, Huang J, Zhang L. Application of artificial intelligence in renal disease. CLINICAL EHEALTH 2021. [DOI: 10.1016/j.ceh.2021.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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Jha A, Yang H, Deng R, Kapp ME, Fogo AB, Huo Y. Instance segmentation for whole slide imaging: end-to-end or detect-then-segment. J Med Imaging (Bellingham) 2021; 8:014001. [PMID: 33426152 PMCID: PMC7790159 DOI: 10.1117/1.jmi.8.1.014001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 12/11/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose: Automatic instance segmentation of glomeruli within kidney whole slide imaging (WSI) is essential for clinical research in renal pathology. In computer vision, the end-to-end instance segmentation methods (e.g., Mask-RCNN) have shown their advantages relative to detect-then-segment approaches by performing complementary detection and segmentation tasks simultaneously. As a result, the end-to-end Mask-RCNN approach has been the de facto standard method in recent glomerular segmentation studies, where downsampling and patch-based techniques are used to properly evaluate the high-resolution images from WSI (e.g., > 10,000 × 10,000 pixels on 40 × ). However, in high-resolution WSI, a single glomerulus itself can be more than 1000 × 1000 pixels in original resolution which yields significant information loss when the corresponding features maps are downsampled to the 28 × 28 resolution via the end-to-end Mask-RCNN pipeline. Approach: We assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework. Beyond such a comparison, we also comprehensively evaluate the performance of our detect-then-segment pipeline through: (1) two of the most prevalent segmentation backbones (U-Net and DeepLab_v3); (2) six different image resolutions ( 512 × 512 , 256 × 256 , 128 × 128 , 64 × 64 , 32 × 32 , and 28 × 28 ); and (3) two different color spaces (RGB and LAB). Results: Our detect-then-segment pipeline, with the DeepLab_v3 segmentation framework operating on previously detected glomeruli of 512 × 512 resolution, achieved a 0.953 Dice similarity coefficient (DSC), compared with a 0.902 DSC from the end-to-end Mask-RCNN pipeline. Further, we found that neither RGB nor LAB color spaces yield better performance when compared against each other in the context of a detect-then-segment framework. Conclusions: The detect-then-segment pipeline achieved better segmentation performance compared with the end-to-end method. Our study provides an extensive quantitative reference for other researchers to select the optimized and most accurate segmentation approach for glomeruli, or other biological objects of similar character, on high-resolution WSI.
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Affiliation(s)
- Aadarsh Jha
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States
| | - Haichun Yang
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States
| | - Ruining Deng
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States
| | - Meghan E. Kapp
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States
| | - Agnes B. Fogo
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, United States
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Noriaki S, Eiichiro U, Yasushi O. Artificial Intelligence in Kidney Pathology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_181-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Yamaguchi R, Kawazoe Y, Shimamoto K, Shinohara E, Tsukamoto T, Shintani-Domoto Y, Nagasu H, Uozaki H, Ushiku T, Nangaku M, Kashihara N, Shimizu A, Nagata M, Ohe K. Glomerular Classification Using Convolutional Neural Networks Based on Defined Annotation Criteria and Concordance Evaluation Among Clinicians. Kidney Int Rep 2020; 6:716-726. [PMID: 33732986 PMCID: PMC7938073 DOI: 10.1016/j.ekir.2020.11.037] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 11/02/2020] [Accepted: 11/30/2020] [Indexed: 11/24/2022] Open
Abstract
Introduction Diagnosing renal pathologies is important for performing treatments. However, classifying every glomerulus is difficult for clinicians; thus, a support system, such as a computer, is required. This paper describes the automatic classification of glomerular images using a convolutional neural network (CNN). Method To generate appropriate labeled data, annotation criteria including 12 features (e.g., “fibrous crescent”) were defined. The concordance among 5 clinicians was evaluated for 100 images using the kappa (κ) coefficient for each feature. Using the annotation criteria, 1 clinician annotated 10,102 images. We trained the CNNs to classify the features with an average κ ≥0.4 and evaluated their performance using the receiver operating characteristic–area under the curve (ROC–AUC). An error analysis was conducted and the gradient-weighted class activation mapping (Grad-CAM) was also applied; it expresses the CNN’s focusing point with a heat map when the CNN classifies the glomerular image for a feature. Results The average κ coefficient of the features ranged from 0.28 to 0.50. The ROC–AUC of the CNNs for test data varied from 0.65 to 0.98. Among the features, “capillary collapse” and “fibrous crescent” had high ROC–AUC values of 0.98 and 0.91, respectively. The error analysis and the Grad-CAM visually showed that the CNN could not distinguish between 2 different features that had similar visual structures or that occurred simultaneously. Conclusion The differences in the texture or frequency of the co-occurrence between the different features affected the CNN performance; thus, to improve the classification accuracy, methods such as segmentation are required.
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Affiliation(s)
- Ryohei Yamaguchi
- Artificial Intelligence in Healthcare, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshimasa Kawazoe
- Artificial Intelligence in Healthcare, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kiminori Shimamoto
- Artificial Intelligence in Healthcare, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Emiko Shinohara
- Artificial Intelligence in Healthcare, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tatsuo Tsukamoto
- Department of Nephrology and Dialysis, Tazuke Kofukai Medical Research Institute, Kitano Hospital, Osaka, Japan
| | - Yukako Shintani-Domoto
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hajime Nagasu
- Department of Nephrology and Hypertension, Kawasaki Medical School, Okayama, Japan
| | - Hiroshi Uozaki
- Department of Pathology, Teikyo University School of Medicine, Tokyo, Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masaomi Nangaku
- Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Naoki Kashihara
- Department of Nephrology and Hypertension, Kawasaki Medical School, Okayama, Japan
| | - Akira Shimizu
- Department of Analytic Human Pathology, Nippon Medical School, Tokyo, Japan
| | - Michio Nagata
- Kidney and Vascular Pathology, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Kazuhiko Ohe
- Department of Biomedical Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
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Wilbur DC, Pettus JR, Smith ML, Cornell LD, Andryushkin A, Wingard R, Wirch E. Using Image Registration and Machine Learning to Develop a Workstation Tool for Rapid Analysis of Glomeruli in Medical Renal Biopsies. J Pathol Inform 2020; 11:37. [PMID: 33343997 PMCID: PMC7737496 DOI: 10.4103/jpi.jpi_49_20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/25/2020] [Accepted: 10/01/2020] [Indexed: 01/15/2023] Open
Abstract
Background Prescreening of biopsies has the potential to improve pathologists' workflow. Tools that identify features and display results in a visually thoughtful manner can enhance efficiency, accuracy, and reproducibility. Machine learning for detection of glomeruli ensures comprehensive assessment and registration of four different stains allows for simultaneous navigation and viewing. Methods Medical renal core biopsies (4 stains each) were digitized using a Leica SCN400 at ×40 and loaded into the Corista Quantum research platform. Glomeruli were manually annotated by pathologists. The tissue on the 4 stains was registered using a combination of keypoint- and intensity-based algorithms, and a 4-panel simultaneous viewing display was created. Using a training cohort, machine learning convolutional neural net (CNN) models were created to identify glomeruli in all stains, and merged into composite fields of views (FOVs). The sensitivity and specificity of glomerulus detection, and FOV area for each detection were calculated. Results Forty-one biopsies were used for training (28) and same-batch evaluation (6). Seven additional biopsies from a temporally different batch were also evaluated. A variant of AlexNet CNN, used for object recognition, showed the best result for the detection of glomeruli with same-batch and different-batch evaluation: Same-batch sensitivity 92%, "modified" specificity 89%, average FOV size represented 0.8% of the total slide area; different-batch sensitivity 90%, "modified" specificity 98% and average FOV size 1.6% of the total slide area. Conclusions Glomerulus detection in the best CNN model shows that machine learning algorithms may be accurate for this task. The added benefit of biopsy registration with simultaneous display and navigation allows reviewers to move from one machine-generated FOV to the next in all 4 stains. Together these features could increase both efficiency and accuracy in the review process.
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Affiliation(s)
| | - Jason R Pettus
- Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | | | - Lynn D Cornell
- Department of Pathology, Mayo Clinic, Rochester, MN, USA
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Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ. Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol 2020; 16:669-685. [PMID: 32848206 PMCID: PMC7447970 DOI: 10.1038/s41581-020-0321-6] [Citation(s) in RCA: 98] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2020] [Indexed: 12/17/2022]
Abstract
The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.
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Affiliation(s)
- Laura Barisoni
- Department of Pathology, Duke University, Durham, NC, USA.
- Department of Medicine, Division of Nephrology, Duke University, Durham, NC, USA.
| | - Kyle J Lafata
- Department of Radiology, Duke University, Durham, NC, USA
- Department of Radiation Oncology, Duke University, Durham, NC, USA
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Veterans Administration Medical Center, Cleveland, OH, USA
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Li F, Nan Y, Hou X, Xie C, Wang J, Lv C, Xie G. Correlation-Guided Network for Fine-Grained Classification of Glomerular lesions in Kidney Histopathology Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5781-5784. [PMID: 33019288 DOI: 10.1109/embc44109.2020.9176234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Chronic Kidney Disease has become a worldwide public health problem which demands careful assessments by pathologists. In this paper, we propose a novel architecture for fine-grained classification of glomerular lesions in renal pathology images sampling from patients with IgA nephropathy. The adversarial correlation loss is innovatively presented to guide a parallel convolutional neural network. In this well- designed loss function, bias between the prediction and the label was take into account while the relationship among different categories is well-aligned. Glomerular lesions in this study are divided into five subcategories, Neg (Negative samples such as tubule and artery), SS (sclerosis involving a portion of the glomerular tuft), GS (sclerosis involving 100% of the tuft), C (build-up of more than two layers of cells within Bowman's space, often with fibrin and collagen deposition) and NOA (none of above). Our model with 93.0% accuracy and 92.9% Fl-score for these five categories has proved superior to other models through experimental results.
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Ligabue G, Pollastri F, Fontana F, Leonelli M, Furci L, Giovanella S, Alfano G, Cappelli G, Testa F, Bolelli F, Grana C, Magistroni R. Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks. Clin J Am Soc Nephrol 2020; 15:1445-1454. [PMID: 32938617 PMCID: PMC7536749 DOI: 10.2215/cjn.03210320] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/03/2020] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS High-magnification (×400) immunofluorescence images of kidney biopsies performed from the year 2001 to 2018 were collected. The report, adopted at the Division of Nephrology of the AOU Policlinico di Modena, describes the specimen in terms of "appearance," "distribution," "location," and "intensity" of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and κ- and λ-light chains. The report was used as ground truth for the training of the convolutional neural networks. RESULTS In total, 12,259 immunofluorescence images of 2542 subjects undergoing kidney biopsy were collected. The test set analysis showed accuracy values between 0.79 ("irregular capillary wall" feature) and 0.94 ("fine granular" feature). The agreement test of the results obtained by the convolutional neural networks with respect to the ground truth showed similar values to three pathologists of our center. Convolutional neural networks were 117 times faster than human evaluators in analyzing 180 test images. A web platform, where it is possible to upload digitized images of immunofluorescence specimens, is available to evaluate the potential of our approach. CONCLUSIONS The data showed that the accuracy of convolutional neural networks is comparable with that of pathologists experienced in the field.
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Affiliation(s)
- Giulia Ligabue
- Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Federico Pollastri
- Department of Engineering "Enzo Ferrari," University of Modena and Reggio Emilia, Modena, Italy
| | - Francesco Fontana
- Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
| | - Marco Leonelli
- Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
| | - Luciana Furci
- Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
| | - Silvia Giovanella
- Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Gaetano Alfano
- Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
| | - Gianni Cappelli
- Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.,Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
| | - Francesca Testa
- Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
| | - Federico Bolelli
- Department of Engineering "Enzo Ferrari," University of Modena and Reggio Emilia, Modena, Italy
| | - Costantino Grana
- Department of Engineering "Enzo Ferrari," University of Modena and Reggio Emilia, Modena, Italy
| | - Riccardo Magistroni
- Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy .,Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Shimada S, Abais-Battad JM, Alsheikh AJ, Yang C, Stumpf M, Kurth T, Mattson DL, Cowley AW. Renal Perfusion Pressure Determines Infiltration of Leukocytes in the Kidney of Rats With Angiotensin II-Induced Hypertension. Hypertension 2020; 76:849-858. [PMID: 32755400 DOI: 10.1161/hypertensionaha.120.15295] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The present study examined the extent to which leukocyte infiltration into the kidneys in Ang II (angiotensin II)-induced hypertension is determined by elevation of renal perfusion pressure (RPP). Male Sprague-Dawley rats were instrumented with carotid and femoral arterial catheters for continuous monitoring of blood pressure and a femoral venous catheter for infusion. An inflatable aortic occluder cuff placed between the renal arteries with computer-driven servo-controller maintained RPP to the left kidney at control levels during 7 days of intravenous Ang II (50 ng/kg per minute) or vehicle (saline) infusion. Rats were fed a 0.4% NaCl diet throughout the study. Ang II-infused rats exhibited nearly a 50 mm Hg increase of RPP (carotid catheter) to the right kidney while RPP to the left kidney (femoral catheter) was controlled at baseline pressure throughout the study. As determined at the end of the studies by flow cytometry, right kidneys exhibited significantly greater numbers of T cells, B cells, and monocytes/macrophages compared with the servo-controlled left kidneys and compared with vehicle treated rats. No difference was found between Ang II servo-controlled left kidneys and vehicle treated kidneys. Immunostaining found that the density of glomeruli, cortical, and outer medullary capillaries were significantly reduced in the right kidney of Ang II-infused rats compared with servo-controlled left kidney. We conclude that in this model of hypertension the elevation of RPP, not Ang II nor dietary salt, leads to leukocyte infiltration in the kidney and to capillary rarefaction.
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Affiliation(s)
- Satoshi Shimada
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
| | | | - Ammar J Alsheikh
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
| | - Chun Yang
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
| | - Megan Stumpf
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
| | - Theresa Kurth
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
| | - David L Mattson
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
| | - Allen W Cowley
- From the Department of Physiology, Medical College of Wisconsin, Milwaukee
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Choi G, Kim YG, Cho H, Kim N, Lee H, Moon KC, Go H. Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy. Mod Pathol 2020; 33:1626-1634. [PMID: 32218521 DOI: 10.1038/s41379-020-0529-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/11/2020] [Accepted: 03/11/2020] [Indexed: 11/09/2022]
Abstract
A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to assess the diagnostic performance of the algorithm by comparing pathologists' diagnoses and analyzing the associations of the algorithm with clinical data. C4d immunostaining slides of renal allografts were obtained from two different institutions (100 slides from the Asan Medical Center and 86 slides from the Seoul National University Hospital) and scanned using two different slide scanners. Three pathologists and the algorithm independently evaluated each slide according to the Banff 2017 criteria. Subsequently, they jointly reviewed the results for consensus scoring. The result of the algorithm was compared with that of each pathologist and the consensus diagnosis. Clinicopathological associations of the results of the algorithm with allograft survival, histologic evidence of microvascular inflammation, and serologic results for donor-specific antibodies were also analyzed. As a result, the reproducibility between the pathologists was fair to moderate (kappa 0.36-0.54), which is comparable to that between the algorithm and each pathologist (kappa 0.34-0.51). The C4d scores predicted by the algorithm achieved substantial concordance with the consensus diagnosis (kappa = 0.61), and they were significantly associated with remarkable microvascular inflammation (P = 0.001), higher detection rate of donor-specific antibody (P = 0.003), and shorter graft survival (P < 0.001). In conclusion, the deep learning-based C4d detection algorithm showed a diagnostic performance similar to that of the pathologists.
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Affiliation(s)
- Gyuheon Choi
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Young-Gon Kim
- Department of Biomedical Engineering, Asan Institute of Life Science, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Haeyon Cho
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Hyunna Lee
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Kyung Chul Moon
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehang-ro, Jongro-gu, Seoul, 03080, South Korea
| | - Heounjeong Go
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.
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63
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Uchino E, Suzuki K, Sato N, Kojima R, Tamada Y, Hiragi S, Yokoi H, Yugami N, Minamiguchi S, Haga H, Yanagita M, Okuno Y. Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach. Int J Med Inform 2020; 141:104231. [PMID: 32682317 DOI: 10.1016/j.ijmedinf.2020.104231] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/30/2020] [Accepted: 07/06/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. METHODS We used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. RESULTS Our model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). CONCLUSION Our study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.
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Affiliation(s)
- Eiichiro Uchino
- Department of Medical Intelligent Systems, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Noriaki Sato
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshinori Tamada
- Department of Medical Intelligent Systems, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shusuke Hiragi
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Division of Medical Informatics and Administration Planning, Kyoto University Hospital, Kyoto, Japan
| | - Hideki Yokoi
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Sachiko Minamiguchi
- Department of Diagnostic Pathology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hironori Haga
- Department of Diagnostic Pathology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Motoko Yanagita
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan.
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan; RIKEN, The Drug Development Data Intelligence Platform Group, Yokohama, Japan.
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64
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Zeng C, Nan Y, Xu F, Lei Q, Li F, Chen T, Liang S, Hou X, Lv B, Liang D, Luo W, Lv C, Li X, Xie G, Liu Z. Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning. J Pathol 2020; 252:53-64. [PMID: 32542677 PMCID: PMC7496925 DOI: 10.1002/path.5491] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 05/26/2020] [Accepted: 06/05/2020] [Indexed: 12/14/2022]
Abstract
Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network‐based mesangial hypercellularity score in periodic acid–Schiff (PAS)‐stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892–0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well‐designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5–11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value < 0.0001) between this analytic renal pathology system (ARPS) and four junior pathologists for identifying mesangial and endothelial cells, while that for podocytes was similar, with P value = 0.0602. In addition, this study indicated that the ratio of mesangial cells, endothelial cells, and podocytes within glomeruli from IgAN was 0.41:0.36:0.23, and the performance of mesangial score assessment reached a Cohen's kappa of 0.42 and 95% CI (0.18, 0.69). The proposed computer‐aided diagnosis system has feasibility for quantitative analysis and auxiliary recognition of glomerular pathological features. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Caihong Zeng
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Yang Nan
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Feng Xu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Qunjuan Lei
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Fengyi Li
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Tingyu Chen
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Shaoshan Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | | | - Bin Lv
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Dandan Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - WeiLi Luo
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Chuanfeng Lv
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Xiang Li
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Guotong Xie
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Zhihong Liu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
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65
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Artificial intelligence and machine learning in nephropathology. Kidney Int 2020; 98:65-75. [PMID: 32475607 DOI: 10.1016/j.kint.2020.02.027] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 01/03/2020] [Accepted: 02/12/2020] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.
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66
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Sealfon RSG, Mariani LH, Kretzler M, Troyanskaya OG. Machine learning, the kidney, and genotype-phenotype analysis. Kidney Int 2020; 97:1141-1149. [PMID: 32359808 PMCID: PMC8048707 DOI: 10.1016/j.kint.2020.02.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 01/13/2020] [Accepted: 02/06/2020] [Indexed: 01/23/2023]
Abstract
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.
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Affiliation(s)
- Rachel S G Sealfon
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Laura H Mariani
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
| | - Olga G Troyanskaya
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
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67
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Bueno G, Fernandez-Carrobles MM, Gonzalez-Lopez L, Deniz O. Glomerulosclerosis identification in whole slide images using semantic segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105273. [PMID: 31891905 DOI: 10.1016/j.cmpb.2019.105273] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 11/12/2019] [Accepted: 12/10/2019] [Indexed: 05/25/2023]
Abstract
BACKGROUND AND OBJECTIVE Glomeruli identification, i.e., detection and characterization, is a key procedure in many nephropathology studies. In this paper, semantic segmentation based on convolutional neural networks (CNN) is proposed to detect glomeruli using Whole Slide Imaging (WSI) follows by a classification CNN to divide the glomeruli into normal and sclerosed. METHODS Comparison between U-Net and SegNet CNNs is performed for pixel-level segmentation considering both a two and three class problem, that is, a) non-glomerular and glomerular structures and b) non-glomerular normal glomerular and sclerotic structures. The two class semantic segmentation result is then used for a CNN classification where glomerular regions are divided into normal and global sclerosed glomeruli. RESULTS These methods were tested on a dataset composed of 47 WSIs belonging to human kidney sections stained with Periodic Acid Schiff (PAS). The best approach was the SegNet for two class segmentation follows by a fine-tuned AlexNet network to characterize the glomeruli. 98.16% of accuracy was obtained with this process of consecutive CNNs (SegNet-AlexNet) for segmentation and classification. CONCLUSION The results obtained demonstrate that the sequential CNN segmentation-classification strategy achieves higher accuracy reducing misclassified cases and therefore being the methodology proposed for glomerulosclerosis detection.
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Affiliation(s)
- Gloria Bueno
- University of Castilla-La Mancha, ETSI Industriales, VISILAB, Ciudad Real, Spain.
| | | | | | - Oscar Deniz
- University of Castilla-La Mancha, ETSI Industriales, VISILAB, Ciudad Real, Spain
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69
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Palygin O, Spires D, Levchenko V, Bohovyk R, Fedoriuk M, Klemens CA, Sykes O, Bukowy JD, Cowley AW, Lazar J, Ilatovskaya DV, Staruschenko A. Progression of diabetic kidney disease in T2DN rats. Am J Physiol Renal Physiol 2019; 317:F1450-F1461. [PMID: 31566426 DOI: 10.1152/ajprenal.00246.2019] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Diabetic kidney disease (DKD) is one of the leading pathological causes of decreased renal function and progression to end-stage kidney failure. To explore and characterize age-related changes in DKD and associated glomerular damage, we used a rat model of type 2 diabetic nephropathy (T2DN) at 12 wk and older than 48 wk. We compared their disease progression with control nondiabetic Wistar and diabetic Goto-Kakizaki (GK) rats. During the early stages of DKD, T2DN and GK animals revealed significant increases in blood glucose and kidney-to-body weight ratio. Both diabetic groups had significantly altered renin-angiotensin-aldosterone system function. Thereafter, during the later stages of disease progression, T2DN rats demonstrated a remarkable increase in renal damage compared with GK and Wistar rats, as indicated by renal hypertrophy, polyuria accompanied by a decrease in urine osmolarity, high cholesterol, a significant prevalence of medullary protein casts, and severe forms of glomerular injury. Urinary nephrin shedding indicated loss of the glomerular slit diaphragm, which also correlates with the dramatic elevation in albuminuria and loss of podocin staining in aged T2DN rats. Furthermore, we used scanning ion microscopy topographical analyses to detect and quantify the pathological remodeling in podocyte foot projections of isolated glomeruli from T2DN animals. In summary, T2DN rats developed renal and physiological abnormalities similar to clinical observations in human patients with DKD, including progressive glomerular damage and a significant decrease in renin-angiotensin-aldosterone system plasma levels, indicating these rats are an excellent model for studying the progression of renal damage in type 2 DKD.
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Affiliation(s)
- Oleg Palygin
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin.,Cardiovascular Center, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Denisha Spires
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Vladislav Levchenko
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ruslan Bohovyk
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Mykhailo Fedoriuk
- 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
| | - Olga Sykes
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - John D Bukowy
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Allen W Cowley
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jozef Lazar
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Daria V Ilatovskaya
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - 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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70
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Affiliation(s)
- Kevin V Lemley
- Division of Nephrology, Children's Hospital Los Angeles, Los Angeles, California; and .,Department of Pediatrics, Keck School of Medicine of the University of Southern California, Los Angeles, California
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71
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Ginley B, Lutnick B, Jen KY, Fogo AB, Jain S, Rosenberg A, Walavalkar V, Wilding G, Tomaszewski JE, Yacoub R, Rossi GM, Sarder P. Computational Segmentation and Classification of Diabetic Glomerulosclerosis. J Am Soc Nephrol 2019; 30:1953-1967. [PMID: 31488606 DOI: 10.1681/asn.2018121259] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 06/17/2019] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Pathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation. METHODS We developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification. RESULTS Our digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen's kappa κ = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with κ1 = 0.68, 95% interval [0.50, 0.86] and κ2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity. CONCLUSIONS Computationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.
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Affiliation(s)
| | | | - Kuang-Yu Jen
- Department of Pathology and Laboratory Medicine, University of California, Davis Medical Center, Sacramento, California
| | - Agnes B Fogo
- Departments of Pathology, Microbiology, and Immunology and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Sanjay Jain
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Avi Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Vighnesh Walavalkar
- Department of Pathology, University of California San Francisco, San Francisco, California; and
| | | | - John E Tomaszewski
- Departments of Pathology and Anatomical Sciences.,Biomedical Informatics, and
| | - Rabi Yacoub
- Division of Nephrology, Department of Medicine, University at Buffalo-The State University of New York, Buffalo, New York
| | - Giovanni Maria Rossi
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.,U.O. Nefrologia, Azienda Ospedaliero-Universitaria di Parma, Dipartimento di Medicina e Chirurgia, Università di Parma
| | - Pinaki Sarder
- Departments of Pathology and Anatomical Sciences, .,Biostatistics.,Biomedical Engineering, and
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72
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Hermsen M, de Bel T, den Boer M, Steenbergen EJ, Kers J, Florquin S, Roelofs JJTH, Stegall MD, Alexander MP, Smith BH, Smeets B, Hilbrands LB, van der Laak JAWM. Deep Learning-Based Histopathologic Assessment of Kidney Tissue. J Am Soc Nephrol 2019; 30:1968-1979. [PMID: 31488607 DOI: 10.1681/asn.2019020144] [Citation(s) in RCA: 192] [Impact Index Per Article: 38.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 07/01/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS). METHODS We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the network's glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. RESULTS The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was "glomeruli" in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by "tubuli combined" and "interstitium." The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. CONCLUSIONS This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.
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Affiliation(s)
| | | | | | | | - Jesper Kers
- Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and.,Center for Analytical Sciences Amsterdam, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands.,The Ragon Institute of the Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
| | - Sandrine Florquin
- Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and
| | - Joris J T H Roelofs
- Department of Pathology, Amsterdam Infection & Immunity, Amsterdam Cardiovascular Sciences, Amsterdam UMC, and
| | - Mark D Stegall
- Divisions of Transplantation surgery.,William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; and
| | - Mariam P Alexander
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; and.,Pathology, and
| | - Byron H Smith
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; and.,Biomedical Statistics and Informatics, and
| | | | - Luuk B Hilbrands
- Nephrology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeroen A W M van der Laak
- Departments of Pathology and .,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
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73
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Maqsood M, Nazir F, Khan U, Aadil F, Jamal H, Mehmood I, Song OY. Transfer Learning Assisted Classification and Detection of Alzheimer's Disease Stages Using 3D MRI Scans. SENSORS 2019; 19:s19112645. [PMID: 31212698 PMCID: PMC6603745 DOI: 10.3390/s19112645] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/03/2019] [Accepted: 06/06/2019] [Indexed: 02/04/2023]
Abstract
Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.
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Affiliation(s)
- Muazzam Maqsood
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
| | - Faria Nazir
- Department of Computer Science, Capital University of Science and Technology, Islamabad 45750, Pakistan.
| | - Umair Khan
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
| | - Farhan Aadil
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
| | - Habibullah Jamal
- Faculty of Engineering Sciences, Ghulam Ishaq Khan Institute, Topi 23460, Pakistan.
| | - Irfan Mehmood
- Department of Media Design and Technology, Faculty of Engineering & Informatics, University of Bradford; Bradford BD7 1DP, UK.
| | - Oh-Young Song
- Department of Software, Sejong University, Seoul 05006, Korea.
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74
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Kuo CC, Chang CM, Liu KT, Lin WK, Chiang HY, Chung CW, Ho MR, Sun PR, Yang RL, Chen KT. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit Med 2019; 2:29. [PMID: 31304376 PMCID: PMC6550224 DOI: 10.1038/s41746-019-0104-2] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 03/19/2019] [Indexed: 12/22/2022] Open
Abstract
Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annotations to remove the peripheral region of the kidneys and applied various data augmentation schemes to produce additional data with variations. Bootstrap aggregation was also applied to avoid overfitting and improve the model's generalization. Moreover, the kidney function features obtained by our deep neural network were used to identify the CKD status defined by an eGFR of <60 ml/min/1.73 m2. A Pearson correlation coefficient of 0.741 indicated the strong relationship between artificial intelligence (AI)- and creatinine-based GFR estimations. Overall CKD status classification accuracy of our model was 85.6% -higher than that of experienced nephrologists (60.3%-80.1%). Our model is the first fundamental step toward realizing the potential of transforming kidney ultrasound imaging into an effective, real-time, distant screening tool. AI-GFR estimation offers the possibility of noninvasive assessment of kidney function, a key goal of AI-powered functional automation in clinical practice.
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Affiliation(s)
- Chin-Chi Kuo
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
- Kidney Institute and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
| | - Chun-Min Chang
- Institute of Information Science, Academia Sinica, Taichung, Taiwan
| | - Kuan-Ting Liu
- Institute of Information Science, Academia Sinica, Taichung, Taiwan
| | - Wei-Kai Lin
- Institute of Information Science, Academia Sinica, Taichung, Taiwan
| | - Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Chih-Wei Chung
- Big Data Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Meng-Ru Ho
- Institute of Information Science, Academia Sinica, Taichung, Taiwan
| | - Pei-Ran Sun
- Information Office, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Rong-Lin Yang
- Information Office, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Kuan-Ta Chen
- Institute of Information Science, Academia Sinica, Taichung, Taiwan
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75
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Kannan S, Morgan LA, Liang B, Cheung MG, Lin CQ, Mun D, Nader RG, Belghasem ME, Henderson JM, Francis JM, Chitalia VC, Kolachalama VB. Segmentation of Glomeruli Within Trichrome Images Using Deep Learning. Kidney Int Rep 2019; 4:955-962. [PMID: 31317118 PMCID: PMC6612039 DOI: 10.1016/j.ekir.2019.04.008] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 04/04/2019] [Accepted: 04/08/2019] [Indexed: 12/22/2022] Open
Abstract
Introduction The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. Methods Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerosed (NPS) glomerulus, and (iii) globally sclerosed (GS) glomerulus. This led to identification of 751 unique images representing nonglomerular regions, 611 images with NPS glomeruli, and 134 images with GS glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test images to segment the GS glomeruli. Results The CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). Conclusion This work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.
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Affiliation(s)
- Shruti Kannan
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Laura A Morgan
- College of Engineering, Boston University, Boston, Massachusetts, USA
| | - Benjamin Liang
- College of Engineering, Boston University, Boston, Massachusetts, USA
| | - McKenzie G Cheung
- College of Engineering, Boston University, Boston, Massachusetts, USA
| | - Christopher Q Lin
- College of Engineering, Boston University, Boston, Massachusetts, USA
| | - Dan Mun
- College of Health & Rehabilitation Sciences, Sargent College, Boston University, Boston, Massachusetts, USA
| | - Ralph G Nader
- Renal Section, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Mostafa E Belghasem
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Joel M Henderson
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Jean M Francis
- Renal Section, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Vipul C Chitalia
- Renal Section, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.,Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.,Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts, USA.,Veterans Administration Boston Healthcare System, Boston, Massachusetts, USA
| | - Vijaya B Kolachalama
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.,Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, Massachusetts, USA.,Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts, USA
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76
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Spires D, Ilatovskaya DV, Levchenko V, North PE, Geurts AM, Palygin O, Staruschenko A. Protective role of Trpc6 knockout in the progression of diabetic kidney disease. Am J Physiol Renal Physiol 2018; 315:F1091-F1097. [PMID: 29923767 DOI: 10.1152/ajprenal.00155.2018] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Diabetic kidney disease (DKD) is a chronic kidney pathology that leads to end-stage renal disease. Previous studies from our laboratory indicate that there is an association between the development of DKD and the transient receptor potential canonical 6 (TRPC6) channel. Trpc6 expression and activity were increased in the streptozotocin (STZ)-treated Dahl Salt-sensitive (Dahl SS) rat, an established model of type 1 diabetes. Here, using a Trpc6 knockout created on the Dahl SS rat background (SSTrpc6-/-), we test the hypothesis that the absence of Trpc6 will protect podocytes and kidney function during the development of DKD. Four groups of animals (control SSWT, SSTrpc6-/-, STZ-treated SSWT, and STZ-SSTrpc6-/-) were utilized in this study. Diabetes development was monitored for 11 wk after STZ injection with periodic weight, glucose, and urinary output measurements. There was an increase in albuminuria and glomerular injury following STZ treatment, which was not different between Dahl SS and SSTrpc6-/- groups. Western blot analysis revealed elevated levels of nephrin in urine samples of STZ-SSWT rats, which was higher compared with STZ-SSTrpc6-/- rats. Furthermore, pathological increases in basal [Ca2+]i levels and foot process damage of podocytes during the development of DKD was attenuated in the STZ-SSTrpc6-/- compared with STZ-SSWT rats. Overall, our data indicate that TRPC6 channel inhibition may have at least partial renoprotective effects, which could lead to the development of new pharmacological tools to treat or prevent the progression of DKD.
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Affiliation(s)
- Denisha Spires
- Department of Physiology, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Daria V Ilatovskaya
- Department of Physiology, Medical College of Wisconsin , Milwaukee, Wisconsin.,Medical University of South Carolina, Department of Medicine, Charleston, South Carolina
| | - Vladislav Levchenko
- Department of Physiology, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Paula E North
- Department of Pathology, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Aron M Geurts
- Department of Physiology, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Oleg Palygin
- Department of Physiology, Medical College of Wisconsin , Milwaukee, Wisconsin
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77
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Affiliation(s)
- Richard Torres
- Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Eben Olson
- Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut
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