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1.
J Am Soc Nephrol ; 2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34479966

RESUMO

Background: Podocyte depletion precedes progressive glomerular damage in several renal diseases. However, the current standard of visual detection and quantification of podocyte nuclei from brightfield microscopy images is laborious and imprecise. Methods: We have developed PodoSighter, an online cloud-based tool, to automatically identify and quantify podocyte nuclei from giga-pixel brightfield whole-slide images (WSIs) using deep learning. Ground-truth to train the tool used immunohistochemically or immunofluorescence-labeled images from a multi-institutional cohort of 122 histologic sections from mouse, rat, and human kidneys. To demonstrate generalizability of our tool in investigating podocyte loss in clinically relevant samples, we tested it in rodent models of glomerular diseases, including diabetic kidney disease, crescentic glomerulonephritis, and dose-dependent direct podocyte toxicity and depletion, as well as in human biopsies from steroid resistant nephrotic syndrome and from human autopsy tissues. Results: The optimal model yielded high sensitivity/specificity of 0.80/0.80, 0.81/0.86, and 0.80/0.91, in mouse, rat, and human images, respectively, from periodic-acid Schiff-stained WSIs. Furthermore, the podocyte nuclear morphometrics extracted using PodoSighter were informative in identifying diseased glomeruli. We have made PodoSighter freely available to the general public as turnkey plugins in a cloud-based web application for end-users. Conclusion: Our study demonstrates an automated computational approach to detect and quantify podocyte nuclei in standard histologically-stained WSIs, facilitating podocyte research and enabling possible future clinical applications.

2.
Artigo em Inglês | MEDLINE | ID: mdl-34366540

RESUMO

Histologic examination of interstitial fibrosis and tubular atrophy (IFTA) is critical to determine the extent of irreversible kidney injury in renal disease. The current clinical standard involves pathologist's visual assessment of IFTA, which is prone to inter-observer variability. To address this diagnostic variability, we designed two case studies (CSs), including seven pathologists, using HistomicsTK- a distributed system developed by Kitware Inc. (Clifton Park, NY). Twenty-five whole slide images (WSIs) were classified into a training set of 21 and a validation set of four. The training set was composed of seven unique subsets, each provided to an individual pathologist along with four common WSIs from the validation set. In CS 1, all pathologists individually annotated IFTA in their respective slides. These annotations were then used to train a deep learning algorithm to computationally segment IFTA. In CS 2, manual and computational annotations from CS 1 were first reviewed by the annotators to improve concordance of IFTA annotation. Both the manual and computational annotation processes were then repeated as in CS1. The inter-observer concordance in the validation set was measured by Krippendorff's alpha (KA). The KA for the seven pathologists in CS1 was 0.62 with CI [0.57, 0.67], and after reviewing each other's annotations in CS2, 0.66 with CI [0.60, 0.72]. The respective CS1 and CS2 KA were 0.58 with CI [0.52, 0.64] and 0.63 with CI [0.56, 0.69] when including the deep learner as an eighth annotator. These results suggest that our designed annotation framework refines agreement of spatial annotation of IFTA and demonstrates a human-AI approach to significantly improve the development of computational models.

3.
Artigo em Inglês | MEDLINE | ID: mdl-34366541

RESUMO

With the rapid advancement in multiplex tissue staining, computer hardware, and machine learning, computationally-based tools are becoming indispensable for the evaluation of digital histopathology. Historically, standard histochemical staining methods such as hematoxylin and eosin, periodic acid-Schiff, and trichrome have been the gold standard for microscopic tissue evaluation by pathologists, and therefore brightfield microscopy images derived from such stains are primarily used for developing computational pathology tools. However, these histochemical stains are nonspecific in terms of highlighting structures and cell types. In contrast, immunohistochemical stains use antibodies to specifically detect and quantify proteins, which can be used to specifically highlight structures and cell types of interest. Traditionally, such immunofluorescence-based methods are only able to simultaneously stain a limited number of target proteins/antigens, typically up to three channels. Fluorescence-based multiplex immunohistochemistry (mIHC) is a new technology that enables simultaneous localization and quantification of numerous proteins/antigens, allowing for the possibility to detect a wide range of histologic structures and cell types within tissue. However, this method is limited by cost, specialized equipment, technical expertise, and time. In this study, we implemented a deep learning-based pipeline to synthetically generate in silico mIHC images from brightfield images of tissue slides-stained with routinely used histochemical stains, in particular PAS. Our tool was trained using fluorescence-based mIHC images as the ground-truth. The proposed pipeline offers high contrast detection of structures in brightfield imaged tissue sections stained with standard histochemical stains. We demonstrate the performance of our pipeline by computationally detecting multiple compartments in kidney biopsies, including cell nuclei, collagen/fibrosis, distal tubules, proximal tubules, endothelial cells, and leukocytes, from PAS-stained tissue sections. Our work can be extended for other histologic structures and tissue types and can be used as a basis for future automated annotation of histologic structures and cell types without the added cost of actually generating mIHC slides.

4.
Artigo em Inglês | MEDLINE | ID: mdl-34366543

RESUMO

In diabetic kidney disease (DKD), podocyte depletion, and the subsequent migration of parietal epithelial cells (PECs) to the tuft, is a precursor to progressive glomerular damage, but the limitations of brightfield microscopy currently preclude direct pathological quantitation of these cells. Here we present an automated approach to podocyte and PEC detection developed using kidney sections from mouse model emulating DKD, stained first for Wilms' Tumor 1 (WT1) (podocyte and PEC marker) by immunofluorescence, then post-stained with periodic acid-Schiff (PAS). A generative adversarial network (GAN)-based pipeline was used to translate these PAS-stained sections into WT1-labeled IF images, enabling in silico label-free podocyte and PEC identification in brightfield images. Our method detected WT1-positive cells with high sensitivity/specificity (0.87/0.92). Additionally, our algorithm performed with a higher Cohen's kappa (0.85) than the average manual identification by three renal pathologists (0.78). We propose that this pipeline will enable accurate detection of WT1-positive cells in research applications.

5.
Nat Commun ; 12(1): 4884, 2021 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-34385460

RESUMO

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost.


Assuntos
Biópsia com Agulha de Grande Calibre/métodos , Aprendizado Profundo , Diagnóstico por Computador/métodos , Nefropatias/patologia , Rim/patologia , Coloração e Rotulagem/métodos , Algoritmos , Corantes/química , Corantes/classificação , Corantes/normas , Diagnóstico Diferencial , Humanos , Nefropatias/diagnóstico , Patologia Clínica/métodos , Patologia Clínica/normas , Padrões de Referência , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Coloração e Rotulagem/normas
6.
Case Rep Nephrol ; 2021: 2586693, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34336318

RESUMO

Classic antiglomerular basement membrane (anti-GBM) disease is an exceedingly rare but extremely aggressive form of glomerulonephritis, typically caused by autoantibodies directed against cryptic, conformational epitopes within the noncollagenous domain of the type IV collagen alpha-3 subunit. Pathologic diagnosis is established by the presence of strong, diffuse, linear staining for immunoglobulin on immunofluorescence microscopy. Recently, patients with atypical clinical and pathologic findings of anti-GBM disease have been described. These patients tend to have an indolent clinical course, without pulmonary involvement, and laboratory testing rarely reveals the presence of anti-GBM antibodies. Specific guidelines for the treatment and management of these patients are unclear. Here, we describe a case of atypical anti-GBM disease in a young child who presented with hematuria and prominent proteinuria. Throughout the course of his illness, creatinine remained normal. He was conservatively treated with steroids and rituximab, resulting in resolution of his clinical symptoms and normalization of laboratory findings.

7.
Transplantation ; 2021 Feb 04.
Artigo em Inglês | MEDLINE | ID: mdl-33560727

RESUMO

BACKGROUND: Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse. METHODS: Deceased donor renal transplants at our institution from 2010-2018 were included. Input data consisted of 21 donor features from United Network for Organ Sharing. A training set composed of ~50%/50% split in DGF-positive and DGF-negative cases was used to generate 400,869 models. Each model was based on one of seven ML algorithms (gradient boosting machine, k-nearest neighbor, logistic regression, neural network, naïve Bayes, random forest, support vector machine) with various combinations of feature sets and hyperparameter values. Each model's performance was based on a separate secondary test dataset and assessed by common statistical metrics. RESULTS: The best performing models were based on neural network algorithms, with the highest area under the receiver operating characteristic (AUROC) curve of 0.7595. This model used 10 out of the original 21 donor features, including age, height, weight, ethnicity, serum creatinine, blood urea nitrogen, hypertension history, donation after cardiac death status, cause of death, and cold ischemia time. With the same donor data, the highest AUROC curve for logistic regression models was 0.7484, using all donor features. CONCLUSION: Our automated en masse ML modeling approach was able to rapidly generate ML models for DGF prediction. The performance of the ML models was comparable to classic logistic regression models.

8.
J Am Soc Nephrol ; 2021 Feb 23.
Artigo em Inglês | MEDLINE | ID: mdl-33622976

RESUMO

BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

9.
Transplant Proc ; 53(4): 1211-1214, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33436168

RESUMO

Coronavirus disease 2019 (COVID-19) is associated with high morbidity and mortality worldwide in both the general population and kidney transplant recipients. Acute kidney injury is a known complication of COVID-19 and appears to most commonly manifest as acute tubular injury on renal biopsy. Coagulopathy associated with COVID-19 is a known but poorly understood complication that has been reported to cause thrombotic microangiopathy on rare occasions in native kidneys of patients with COVID-19. Here, we report the first case of biopsy-proven thrombotic microangiopathy in a kidney transplant recipient with COVID-19 who developed acute pancreatitis and clinical features of microangiopathic hemolytic anemia. The patient recovered with supportive care alone.


Assuntos
COVID-19/diagnóstico , Transplante de Rim/efeitos adversos , Pancreatite/etiologia , Microangiopatias Trombóticas/etiologia , COVID-19/complicações , COVID-19/virologia , Creatinina/sangue , Feminino , Humanos , Rim/patologia , Falência Renal Crônica/complicações , Falência Renal Crônica/terapia , Pessoa de Meia-Idade , Pancreatite/diagnóstico , Contagem de Plaquetas , SARS-CoV-2/isolamento & purificação , Tacrolimo/sangue , Tacrolimo/uso terapêutico , Microangiopatias Trombóticas/diagnóstico , Transplante Homólogo/efeitos adversos
10.
Transplantation ; 105(5): 1069-1076, 2021 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-32639410

RESUMO

BACKGROUND: The vast majority of polyomavirus nephropathy (PVN) is due to BK virus, but rare cases result from JC virus reactivation. To date, only a handful of biopsy-proven JC-PVN cases have been reported. Here, we describe the clinical and pathologic findings in 7 patients with biopsy-proven JC-PVN. METHODS: Search of the pathology archives at 2 institutions found 7 cases of JC-PVN. Clinical data were extracted from the electronic medical records, and the biopsies were reviewed. RESULTS: Four cases were diagnosed at 6 y posttransplant or later. The remaining 3 cases presented within approximately 2 y posttransplant, of which 2 showed subclinical JC-PVN on surveillance biopsy. Two early presenting patients were treated for acute rejection just before acquiring JC-PVN. Late presenting patients had higher chronicity, which correlated to worse outcome. All but 1 biopsy showed nonspecific inflammation within areas of interstitial fibrosis without significant inflammation in unscarred cortex. The earliest presenting patient was the exception and showed active inflammation with tubulitis. Viral cytopathic changes were detected in all cases with moderate or high-histologic viral load (pvl), showing preference for the distal tubules and medulla. The 2 cases with low pvl did not demonstrate cytopathic changes but were SV40 positive. CONCLUSIONS: JC-PVN can be insidious in presentation, which may cause delayed or missed diagnosis. Unlike BK-PVN, which typically occurs early in the posttransplant period, JC-PVN can occur both early and late following transplant. Overreliance on negative plasma and urine BK viral loads to exclude PVN can be a pitfall.

11.
Sci Rep ; 10(1): 11064, 2020 07 06.
Artigo em Inglês | MEDLINE | ID: mdl-32632119

RESUMO

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen's kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice.


Assuntos
Aprendizado Profundo , Neoplasias Gastrointestinais/patologia , Gradação de Tumores/métodos , Tumores Neuroendócrinos/patologia , Neoplasias Gastrointestinais/metabolismo , Humanos , Imuno-Histoquímica , Antígeno Ki-67/metabolismo , Gradação de Tumores/estatística & dados numéricos , Tumores Neuroendócrinos/metabolismo , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Sinaptofisina/metabolismo
12.
Transpl Infect Dis ; 22(5): e13347, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32495980

RESUMO

Kaposi sarcoma (KS) is a vascular neoplasm caused by human herpesvirus-8 (HHV-8) infection. KS is most often seen in individuals with acquired immunodeficiency syndrome but can occur in patients who are on immunosuppressive therapy. While the skin and oral mucosa are the typical sites for KS, lesions of the tonsil are quite rare with only a few reported cases. Here, we present a case of tonsillar KS occurring in a renal transplant patient. He presented with dysphagia, odynophagia, and weight loss. Oral examination revealed tonsillar hypertrophy with purple discoloration. Imaging revealed diffuse enlargement of Waldeyer's ring with enlarged right cervical lymph nodes, worrisome for post-transplant lymphoproliferative disorder. Microscopic examination of the tonsillectomy specimen showed a vascular proliferation positive for HHV-8, consistent with KS. The patient was subsequently treated with immunosuppression reduction and the addition of sirolimus, which resulted in complete resolution of oropharyngeal and cervical lesions.

13.
Artigo em Inglês | MEDLINE | ID: mdl-32362708

RESUMO

Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen's kappa values of 0.87 and 0.78 respectfully.

14.
Artigo em Inglês | MEDLINE | ID: mdl-32382209

RESUMO

Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient's glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis. Classification of different stages of DN by pathologists is based on semi-qualitative assessments of these characteristics on an individual glomerulus basis. Being able to probabilistically infer stage membership of individual glomeruli based on a combination of easily observable and hidden image features would be an invaluable tool for furthering our understanding of the drivers of DN progression. Markov Particle filters, included in the bnlearn package in R, were used to query a Bayesian Network (BN) constructed using the structural Hill-Climbing algorithm on a set of glomerular features. These features included both traditional characteristics such as glomerular area and number of mesangial nuclei as well as more abstract features derived from Minimum Spanning Trees (MST) to quantify spatial distribution of mesangial nuclei. Our results using images from multiple institutions suggest that these abstract features exercise a variable influence on DN stage membership over the course of disease progression. Further research incorporating clinical data will give nephrologists a "white box" visual of quantitative factors present in DN patients.

15.
Case Rep Nephrol ; 2020: 2638283, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32257470

RESUMO

Nivolumab is an immune checkpoint inhibitor that targets programmed death-1 on T cells and is designed to amplify an immunologic reaction against cancer cells. However, upregulation of the immune system with checkpoint inhibition is nonspecific, and it can be associated with certain renal side effects, the best documented of which is acute tubulointerstitial nephritis. We present a unique case of a patient with acute kidney injury associated with nephrotic syndrome shortly after starting nivolumab therapy for metastatic anal carcinoma. Subsequent renal biopsy revealed membranoproliferative glomerulonephritis (MPGN). We believe this represents the first reported direct case of nivolumab-associated MPGN. As immunotherapy becomes more widely used in cancer treatment, particular attention must be paid to possible consequences of immune checkpoint inhibitors.

16.
AJR Am J Roentgenol ; 215(1): 148-152, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32097029

RESUMO

OBJECTIVE. The objective of our study was to investigate the significance of sonographic features in assessing for acute kidney allograft rejection in the modern era. MATERIALS AND METHODS. In this retrospective study, 107 adult patients with a kidney allograft biopsy performed between 2015 and 2018 and diagnostic ultrasound performed within 2 weeks of the biopsy were included. Acute rejection was diagnosed on the basis of biopsy tissue sample results using the Banff criteria. The following ultrasound features were assessed: perfusion, cortical echogenicity, corticomedullary differentiation, urothelial thickening, change in renal length, renal artery velocity, and intraparenchymal arterial resistive index. Subjective measures of perfusion, echogenicity, corticomedullary differentiation, and urothelial thickening were assessed independently and in consensus by three abdominal radiologists; multirater kappa values were calculated for interobserver variability. The Wilcoxon rank sum test and chi-square test were used to evaluate the differences between two groups (rejection vs no rejection) and the sonographic features. Sensitivity, specificity, positive predictive value, and negative predictive value (NPV) were calculated for sonographic features that are associated with acute rejection. RESULTS. Of the sonographic features, only the presence of urothelial thickening was significantly associated with acute rejection (p < 0.001) and had substantial agreement (κ = 0.61) among readers. Urothelial thickening was highly sensitive (96%; 95% CI, 79-100%) with a high NPV (98%; 95% CI, 86-100%). CONCLUSION. Urothelial thickening on ultrasound is a highly sensitive finding for acute kidney rejection with a high NPV and thus may play a role in sonographic prebiopsy screening. Other historically associated sonographic features seem to play little, if any, role in the screening and assessment for kidney allograft rejection in the modern era.


Assuntos
Rejeição de Enxerto/diagnóstico por imagem , Transplante de Rim , Ultrassonografia/métodos , Urotélio/diagnóstico por imagem , Urotélio/patologia , Adulto , Idoso , Aloenxertos , Biópsia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Estudos Retrospectivos , Sensibilidade e Especificidade
17.
Arch Pathol Lab Med ; 144(10): 1245-1253, 2020 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-32057275

RESUMO

CONTEXT.­: The adoption of digital capture of pathology slides as whole slide images (WSI) for educational and research applications has proven utility. OBJECTIVE.­: To compare pathologists' primary diagnoses derived from WSI versus the standard microscope. Because WSIs differ in format and method of observation compared with the current standard glass slide microscopy, this study is critical to potential clinical adoption of digital pathology. DESIGN.­: The study enrolled a total of 2045 cases enriched for more difficult diagnostic categories and represented as 5849 slides were curated and provided for diagnosis by a team of 19 reading pathologists separately as WSI or as glass slides viewed by light microscope. Cases were reviewed by each pathologist in both modalities in randomized order with a minimum 31-day washout between modality reads for each case. Each diagnosis was compared with the original clinical reference diagnosis by an independent central adjudication review. RESULTS.­: The overall major discrepancy rates were 3.64% for WSI review and 3.20% for manual slide review diagnosis methods, a difference of 0.44% (95% CI, -0.15 to 1.03). The time to review a case averaged 5.20 minutes for WSI and 4.95 minutes for glass slides. There was no specific subset of diagnostic category that showed higher rates of modality-specific discrepancy, though some categories showed greater discrepancy than others in both modalities. CONCLUSIONS.­: WSIs are noninferior to traditional glass slides for primary diagnosis in anatomic pathology.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Microscopia/métodos , Patologia Cirúrgica/métodos , Método Duplo-Cego , Humanos , Variações Dependentes do Observador , Reprodutibilidade dos Testes
18.
Nat Commun ; 11(1): 394, 2020 01 20.
Artigo em Inglês | MEDLINE | ID: mdl-31959748

RESUMO

Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high (56Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours.


Assuntos
Carcinogênese/efeitos da radiação , Instabilidade Genômica/efeitos da radiação , Neoplasias Induzidas por Radiação/genética , Lesões Experimentais por Radiação/genética , Proteína Supressora de Tumor p53/genética , Animais , Carcinogênese/genética , Dano ao DNA/efeitos da radiação , Análise Mutacional de DNA , Relação Dose-Resposta à Radiação , Feminino , Amplificação de Genes/efeitos da radiação , Mutação em Linhagem Germinativa , Humanos , Masculino , Camundongos , Camundongos Knockout , Neoplasias Induzidas por Radiação/patologia , Mutação Puntual/efeitos da radiação , Proteínas Proto-Oncogênicas c-met/genética , Lesões Experimentais por Radiação/patologia , Sequenciamento Completo do Genoma
19.
Biomed Opt Express ; 10(12): 6516-6530, 2019 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-31853414

RESUMO

In the clinical practice of pathology, trichrome stains are commonly used to highlight collagen and to help evaluate fibrosis. Such stains do delineate collagen deposits but are not molecularly specific and can suffer from staining inconsistencies. Moreover, performing histochemical stain evaluation requires the preparation of additional sections beyond the original hematoxylin- and eosin-stained slides, as well as additional staining steps, which together add cost, time, and workflow complications. We have developed a new microscopy approach, termed DUET (DUal-mode Emission and Transmission) that can be used to extract signals that would typically require special stains or advanced optical methods. Our preliminary analysis demonstrates the potential of using the resulting signals to generate virtual histochemical images that resemble trichrome-stained slides and can support clinical evaluation. We demonstrate advantages of this approach over images acquired from conventional trichrome-stained slides and compare them with images created using second harmonic generation microscopy.

20.
J Am Soc Nephrol ; 30(10): 1953-1967, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31488606

RESUMO

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.


Assuntos
Nefropatias Diabéticas/classificação , Nefropatias Diabéticas/patologia , Diagnóstico por Computador , Glomérulos Renais/patologia , Humanos
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