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Contains fulltext : 299840.pdf (Publisher’s version ) (Open Access), The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

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Contains fulltext : 283491.pdf (Publisher’s version ) (Open Access), BACKGROUND: Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy. METHODS: A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms "kidney", "biopsy", "transplantation" and "artificial intelligence" and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study. RESULTS: Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising. CONCLUSION: All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidne

Item does not contain fulltext, INTRODUCTION: Immunostaining with p16(INK4a) (p16), a tumor-suppressor surrogate protein biomarker for high-risk human papillomavirus (hrHPV) oncogenic activity, may complement standard hematoxylin and eosin (H&E) histology review, and provide more objective criteria to support the cervical intraepithelial neoplasia (CIN) diagnosis. With this study we assessed the impact of p16 immunohistochemistry on CIN grading in an hrHPV-based screening setting. MATERIAL AND METHODS: In this post-hoc analysis, 326 histology follow-up samples from a group of hrHPV-positive women were stained with p16 immunohistochemistry. All H&E samples were centrally revised. The pathologists reported their level of confidence in classifying the CIN lesion. RESULTS: Combining H&E and p16 staining resulted in a change of diagnosis in 27.3% (n = 89) of cases compared with the revised H&E samples, with a decrease of 34.5% (n = 18) in CIN1 and 22.7% (n = 15) in CIN2 classifications, and an increase of 18.3% (n = 19) in no CIN and 20.7% (n = 19) in CIN3 diagnoses. The level of confidence in CIN grading by the pathologist increased with adjunctive use of p16 immunohistochemistry to standard H&E. CONCLUSIONS: This study shows that adjunctive use of p16 immunohistochemistry to H&E morphology reduces the number of CIN1 and CIN2 classifications with a proportional increase in no CIN and CIN3 diagnoses, compared with standard H&E-based CIN diagnosis alone. The pathologists felt more confident in classifying the material with H&E and p16 immunohistochemistry than by using H&E alone, particularly during assessment of small biopsies. Adjunctive use of p16 immunohistochemistry to standard H&E assessment of CIN would be valuable for the diagnostic accuracy, thereby optimizing CIN management and possibly decreasing overtreatment.

Item does not contain fulltext, In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3(+) cell density within scarred regions and higher CD3(+) cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.

Contains fulltext : 238916.pdf (Publisher’s version ) (Open Access), Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (<10% versus ≥10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163(+) cell density was higher in patients with ≥10% IFTA development 6 months post-transplantation (p < 0.05). CD3(+)CD8(-)/CD3(+)CD8(+) ratios were higher in patients with <10% IFTA development (p < 0.05). We observed a high correlation between CD163(+)

Contains fulltext : 209655.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 201136.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 193420.pdf (publisher's version ) (Open Access), Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

Item does not contain fulltext, OBJECTIVE: To assess risk factors for nasopharyngeal carriage of potential pathogens in geographically isolated Warao Amerindians in Venezuela. METHODS: In this point prevalence survey, nasopharyngeal swabs were obtained from 1064 Warao Amerindians: 504 children aged 0-4 years, 227 children aged 5-10 years and 333 caregivers. Written questionnaires were completed to obtain information on demographics and environmental risk factors. Anthropometric measurements were performed in children aged 0-4 years. RESULTS: Carriage rates of Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae and Moraxella catarrhalis were 51%, 7%, 1% and 13%, respectively. Crowding index, method of cooking and tobacco exposure were not associated with increased carriage. In multivariable analysis, an increase in height-for-age Z score (i.e. improved chronic nutritional status) was associated with decreased odds of S. pneumoniae colonisation (OR 0.76, 95% CI 0.70-0.83) in children aged 0-4 years. CONCLUSIONS: Better knowledge of demographic and environmental risk factors facilitates better understanding of the dynamics of colonisation with respiratory bacteria in an Amerindian population. Poor chronic nutritional status was associated with increased pathogen carriage in children <5 years of age. The high rates of stunting generally observed in indigenous children may fuel the acquisition of respiratory bacteria that can lead to respiratory and invasive disease.

Contains fulltext : 181885.pdf (Publisher’s version ) (Open Access), Currently, histopathological tissue examination by a pathologist represents the gold standard for breast lesion diagnostics. Automated classification of histopathological whole-slide images (WSIs) is challenging owing to the wide range of appearances of benign lesions and the visual similarity of ductal carcinoma in-situ (DCIS) to invasive lesions at the cellular level. Consequently, analysis of tissue at high resolutions with a large contextual area is necessary. We present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, DCIS, and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of hematoxylin and eosin stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of nonmalignant and malignant slides and obtains a three-class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potential for routine diagnostics.

Contains fulltext : 174739.pdf (publisher's version ) (Closed access), The aim of this study was to evaluate the clinical utility of p16/Ki-67 dual staining, for the identification of CIN in high-risk HPV-positive women from a non-responder screening cohort. P16/Ki-67 dual staining, Pap cytology, and HPV16/18 genotyping were performed on physician-taken liquid-based samples from 495 women who tested high-risk HPV positive on self-sampled material (PROHTECT-3B study). Different triage strategies involving p16/Ki-67 dual staining were evaluated for sensitivity, specificity, and predictive value for >/=CIN2 and >/=CIN3, and compared to Pap cytology with a threshold of atypical cells of undetermined significance. Centrally revised histology or an adjusted endpoint with combined high-risk HPV negative and cytology negative follow-up at 6 months was used as gold standard. Pap cytology (threshold atypical cells of undetermined significance) triage of high-risk HPV-positive samples showed a sensitivity of 93% (95% confidence interval: 85-98) with a specificity of 49% (95% confidence interval: 41-56) for >/=CIN3. Three triage strategies with p16/Ki-67 showed a significantly increased specificity with similar sensitivity. P16/Ki-67 triage of all high-risk HPV-positive samples had a sensitivity of 92% (95% confidence interval: 84-97) and a specificity of 61% (95% confidence interval: 54-69) for >/=CIN3. Applying p16/Ki-67 triage to only high-risk HPV-positive women with low-grade Pap cytology showed a similar sensitivity of 92% (95% confidence interval: 84-97), with a specificity for >/=CIN3 of 64% (95% confidence interval: 56-71). For high-risk HPV-positive women with low-grade and normal Pap cytology, triage with p16/Ki-67 showed a sensitivity of 96% (95% confidence interval: 89-99), and a specificity of 58% (95% confidence interval: 50-65). HPV16/18 genotyping combined with Pap cytology showed a sensitivity and specificity for >/=CIN3 similar to Pap cytology with an atypical cells of undetermined significance threshold. Because the quality of