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Klinkhammer BM, Boor P. Kidney fibrosis: Emerging diagnostic and therapeutic strategies. Mol Aspects Med 2023; 93:101206. [PMID: 37541106 DOI: 10.1016/j.mam.2023.101206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/25/2023] [Indexed: 08/06/2023]
Abstract
An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.
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Affiliation(s)
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany; Division of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany.
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2
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Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [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: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
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Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
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3
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Ricaurte Archila L, Denic A, Mullan AF, Narasimhan R, Bogojevic M, Thompson RH, Leibovich BC, Sangaralingham SJ, Smith ML, Alexander MP, Rule AD. A Higher Foci Density of Interstitial Fibrosis and Tubular Atrophy Predicts Progressive CKD after a Radical Nephrectomy for Tumor. J Am Soc Nephrol 2021; 32:2623-2633. [PMID: 34531177 PMCID: PMC8722813 DOI: 10.1681/asn.2021020267] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/22/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Chronic tubulointerstitial injury on kidney biopsy is usually quantified by the percentage of cortex with interstitial fibrosis/tubular atrophy (IF/TA). Whether other patterns of IF/TA or inflammation in the tubulointerstitium have prognostic importance beyond percentage IF/TA is unclear. METHODS We obtained, stained, and digitally scanned full cortical thickness wedge sections of renal parenchyma from patients who underwent a radical nephrectomy for a tumor over 2000-2015, and morphometrically analyzed the tubulointerstitium of the cortex for percentage IF/TA, IF/TA density (foci per mm2 cortex), percentage subcapsular IF/TA, striped IF/TA, percentage inflammation (both within and outside IF/TA regions), and percentage subcapsular inflammation. Patients were followed with visits every 6-12 months. Progressive CKD was defined as dialysis, kidney transplantation, or 40% decline from the postnephrectomy eGFR. Cox models assessed the risk of CKD or noncancer mortality with morphometric measures of tubulointerstitial injury after adjustment for the percentage IF/TA and clinical characteristics. RESULTS Among 936 patients (mean age, 64 years; postnephrectomy baseline eGFR, 48 ml/min per 1.73m2), 117 progressive CKD events and 183 noncancer deaths occurred over a median 6.4 years. Higher IF/TA density predicted both progressive CKD and noncancer mortality after adjustment for percentage IF/TA and predicted progressive CKD after further adjustment for clinical characteristics. Independent of percentage IF/TA, age, and sex, higher IF/TA density correlated with lower eGFR, smaller nonsclerosed glomeruli, more global glomerulosclerosis, and smaller total cortical volume. CONCLUSIONS Higher density of IF/TA foci (a more scattered pattern with more and smaller foci) predicts higher risk of progressive CKD after radical nephrectomy compared with the same percentage of IF/TA but with fewer and larger foci.
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Affiliation(s)
| | - Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Aidan F. Mullan
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Ramya Narasimhan
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Marija Bogojevic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Maxwell L. Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Mariam P. Alexander
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | - Andrew D. Rule
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota,Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
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4
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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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5
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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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6
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Courtoy GE, Leclercq I, Froidure A, Schiano G, Morelle J, Devuyst O, Huaux F, Bouzin C. Digital Image Analysis of Picrosirius Red Staining: A Robust Method for Multi-Organ Fibrosis Quantification and Characterization. Biomolecules 2020; 10:biom10111585. [PMID: 33266431 PMCID: PMC7709042 DOI: 10.3390/biom10111585] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/19/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023] Open
Abstract
Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods are operator-dependent, organ-specific, and/or need advanced equipment. Therefore, we developed a robust, minimally operator-dependent, and tissue-transposable digital method for fibrosis quantification. The proposed method involves a novel algorithm for more specific and more sensitive detection of collagen fibers stained by picrosirius red (PSR), a computer-assisted segmentation of histological structures, and a new automated morphological classification of fibers according to their compactness. The new algorithm proved more accurate than classical filtering using principal color component (red-green-blue; RGB) for PSR detection. We applied this new method on established mouse models of liver, lung, and kidney fibrosis and demonstrated its validity by evidencing topological collagen accumulation in relevant histological compartments. Our data also showed an overall accumulation of compact fibers concomitant with worsening fibrosis and evidenced topological changes in fiber compactness proper to each model. In conclusion, we describe here a robust digital method for fibrosis analysis allowing accurate quantification, pattern recognition, and multi-organ comparisons useful to understand fibrosis dynamics.
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Affiliation(s)
- Guillaume E. Courtoy
- IREC Imaging Platform (2IP), Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium;
| | - Isabelle Leclercq
- Laboratory of Hepato-Gastroenterology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
- Correspondence: (I.L.); (C.B.)
| | - Antoine Froidure
- Pole of Pneumology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium;
| | - Guglielmo Schiano
- Mechanisms of Inherited Kidney Diseases Group, University of Zurich, 8057 Zurich, Switzerland; (G.S.); (O.D.)
| | - Johann Morelle
- Pole of Nephrology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium;
| | - Olivier Devuyst
- Mechanisms of Inherited Kidney Diseases Group, University of Zurich, 8057 Zurich, Switzerland; (G.S.); (O.D.)
- Pole of Nephrology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium;
| | - François Huaux
- Louvain Centre for Toxicology and Applied Pharmacology, Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium;
| | - Caroline Bouzin
- IREC Imaging Platform (2IP), Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium;
- Correspondence: (I.L.); (C.B.)
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Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys. ELECTRONICS 2020. [DOI: 10.3390/electronics9101644] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments.
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8
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Severity assessment in mice subjected to carbon tetrachloride. Sci Rep 2020; 10:15790. [PMID: 32978437 PMCID: PMC7519684 DOI: 10.1038/s41598-020-72801-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/01/2020] [Indexed: 01/28/2023] Open
Abstract
The Directive 2010/63 EU requires classifying burden and severity in all procedures using laboratory animals. This study evaluated the severity of liver fibrosis induction by intraperitoneal carbon tetrachloride (CCl4) injections in mice. 29 male C57BL/6N mice were treated three times per week for 4 weeks with an intraperitoneal injection (50 µl) of either 0.6 ml/kg body weight CCl4-vehicle solution, germ oil (vehicle-control) or handling only. Severity assessment was performed using serum analysis, behavioral tests (open field test, rotarod, burrowing and nesting behavior), fecal corticosterone metabolite (FCM) measurement, and survival. The most significant group differences were noticed in the second week of treatment when the highest AST (1463 ± 1404 vs. 123.8 ± 93 U/L, p < 0.0001) and nesting values were measured. In addition, respective animals showed lower moving distances (4622 ± 1577 vs. 6157 ± 2060 cm, p < 0.01) and velocity in the Open field, identified as main factors in principal component analysis (PCA). Overall, a 50% survival rate was observed within the treatment group, in which the open field performance was a good tracer parameter for survival. In summary, this study demonstrates the feasibility of assessing severity in mice using behavioral tests and highlight the open field test as a possible threshold parameter for risk assessment of mortality.
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9
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Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of Artificial Intelligence in Kidney Disease. Int J Med Sci 2020; 17:970-984. [PMID: 32308551 PMCID: PMC7163364 DOI: 10.7150/ijms.42078] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 03/17/2020] [Indexed: 12/17/2022] Open
Abstract
Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.
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Affiliation(s)
- Qiongjing Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Haixia Zhang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China.,Department of Nephrology, Second Affiliated Hospital of Soochow University, 1055 Sanxiang Road, Suzhou, Jiangsu 215000, China
| | - Tianci Deng
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Shumei Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangning Yuan
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Wenbin Tang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Yanyun Xie
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Huipeng Ge
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiufen Wang
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Qiaoling Zhou
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, China
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10
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Pre-implantation kidney biopsy: value of the expertise in determining histological score and comparison with the whole organ on a series of discarded kidneys. J Nephrol 2019; 33:167-176. [PMID: 31471818 DOI: 10.1007/s40620-019-00638-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/10/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Evidence about the reliability of pre-implantation biopsy is still conflicting, depending on both biopsy type and pathologist's expertise. Aim of the study is to evaluate the agreement of general v specialist pathologists and to compare scores on biopsy and whole organs in a set of discarded kidneys. METHODS 46 discarded kidneys were identified with their corresponding biopsies. The biopsies were reviewed by three general and two specialist pathologists, blinded to the original report, according to Remuzzi score. The intraclass correlation coefficient (ICC) was calculated for both groups. Discarded kidneys were scored according to Remuzzi score by a single specialist pathologist. Biopsies and organs were compared by Wilcoxon signed rank test. Weighted κ coefficients between biopsy and organ scores were also calculated. RESULTS Specialist pathologists achieved higher values of ICC, reaching excellent or good agreement in most of the parameters, while general pathologists values were mainly fair or good. On whole organs, scores were consistently lower than biopsies, with a significant difference in most of the parameters. Weighted κ coefficient was slight or fair for most of the parameters. CONCLUSIONS Our data suggests that the creation of a pool of specialist pathologists would improve organ utilization. Moreover, biopsies are not representative of the whole organ. As the Remuzzi score on biopsy is a major reasons for discard, a quota of transplantable kidneys may be erroneously discarded. Refinement in Remuzzi cut-offs based on expert reporting and recognition of sampling error of biopsies in correlation with clinical outcome data should be undertaken.
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11
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Bülow RD, Boor P. Extracellular Matrix in Kidney Fibrosis: More Than Just a Scaffold. J Histochem Cytochem 2019; 67:643-661. [PMID: 31116062 DOI: 10.1369/0022155419849388] [Citation(s) in RCA: 200] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Kidney fibrosis is the common histological end-point of progressive, chronic kidney diseases (CKDs) regardless of the underlying etiology. The hallmark of renal fibrosis, similar to all other organs, is pathological deposition of extracellular matrix (ECM). Renal ECM is a complex network of collagens, elastin, and several glycoproteins and proteoglycans forming basal membranes and interstitial space. Several ECM functions beyond providing a scaffold and organ stability are being increasingly recognized, for example, in inflammation. ECM composition is determined by the function of each of the histological compartments of the kidney, that is, glomeruli, tubulo-interstitium, and vessels. Renal ECM is a dynamic structure undergoing remodeling, particularly during fibrosis. From a clinical perspective, ECM proteins are directly involved in several rare renal diseases and indirectly in CKD progression during renal fibrosis. ECM proteins could serve as specific non-invasive biomarkers of fibrosis and scaffolds in regenerative medicine. The gold standard and currently only specific means to measure renal fibrosis is renal biopsy, but new diagnostic approaches are appearing. Here, we discuss the localization, function, and remodeling of major renal ECM components in healthy and diseased, fibrotic kidneys and the potential use of ECM in diagnostics of renal fibrosis and in tissue engineering.
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Affiliation(s)
- Roman David Bülow
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.,Department of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany
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12
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Ahmady Phoulady H, Goldgof D, Hall LO, Nash KR, Mouton PR. Automatic stereology of mean nuclear size of neurons using an active contour framework. J Chem Neuroanat 2019; 96:110-115. [PMID: 30630013 DOI: 10.1016/j.jchemneu.2018.12.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 12/31/2018] [Accepted: 12/31/2018] [Indexed: 01/20/2023]
Abstract
The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats. Animals were perfused in-vivo with 4% paraformaldehyde and sectioned by frozen microtomy at an instrument setting of 40 μm. For each rat brain, a systematic-random set of sections through the entire substantia nigra pars compacta (SN) were immunostained to reveal tyrosine hydroxylase (TH)-immunopositive neurons. The novel framework applied an active contour (modified balloon snake) model with non-constant balloon force to automatically segment and quantify neuronal cell bodies by stereological point counting (SPC). Several contours were initialized in the image and based on the contour fit after 200 iterations classified as immunopositive (signal) or background contours in a sequential manner. Cell contours were determined in four steps based on several criteria, e.g., area of contour, dispersion measure, and degree of overlap. The image was automatically segmented according to the final contours. Using a point grid automatically generated at systematic-random orientations over the images, points hitting the segmented neural cell bodies were automatically counted. The final values from the automatic framework were compared with findings for ground truth (manual SPC). The results of this study show a strong agreement between data collected by the automatic framework and the ground truth (R2 ≥ 0.95) with a 5× gain in time efficiency for the automatic SPC. These findings give strong support for future applications of pattern recognition for assessing stereological parameters of biological objects identified by high signal:noise stains.
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Affiliation(s)
- Hady Ahmady Phoulady
- Department of Computer Science, University of Southern Maine, Portland, ME, United States.
| | - Dmitry Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, United States
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL, United States
| | - Kevin R Nash
- Department of Molecular Pharmacology and Physiology, University of South Florida, Tampa, FL, United States
| | - Peter R Mouton
- Byrd Alzheimer's Disease Center and Research Institute, University of South Florida, Tampa, FL, United States; SRC Biosciences, Tampa, FL, United States
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