Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

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.

Human scattered tubular cells represent a heterogeneous population of glycolytic dedifferentiated proximal tubule cells.

Scattered tubular cells (STCs) are a phenotypically distinct cell population in the proximal tubule that increase in number after acute kidney injury. We aimed to characterize the human STC population. Three-dimensional human tissue analysis revealed that STCs are preferentially located within inner bends of the tubule and are barely present in young kidney tissue (<2 years), and their number increases with age. Increased STC numbers were associated with acute tubular injury (kidney injury molecule 1) and interstitial fibrosis (alpha smooth muscle actin). Isolated CD13+ CD24- CD133- proximal tubule epithelial cells (PTECs) and CD13+ CD24+ and CD13+ CD133+ STCs were analyzed using RNA sequencing. Transcriptome analysis revealed an upregulation of nuclear factor κB, tumor necrosis factor alpha, and inflammatory pathways in STCs, whereas metabolism, especially the tricarboxylic acid cycle and oxidative phosphorylation, was downregulated, without showing signs of cellular senescence. Using immunostaining and a publicly available single-cell sequencing database of human kidneys, we demonstrate that STCs represent a heterogeneous population in a transient state. In conclusion, STCs are dedifferentiated PTECs showing a metabolic shift toward glycolysis, which could facilitate cellular survival after kidney injury. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Automated scoring of total inflammation in renal allograft biopsies.

BACKGROUND: Computer-assisted scoring is gaining prominence in the evaluation of renal histology; however, much of the focus has been on identifying larger objects such as glomeruli. Total inflammation impacts graft outcome, and its quantification requires tools to identify objects at the cellular level or smaller. The goal of the current study was to use CD45 stained slides coupled with image analysis tools to quantify the amount of non-glomerular inflammation within the cortex. METHODS: Sixty renal transplant whole slide images were used for digital image analysis. Multiple thresholding methods using pixel intensity and object size were used to identify inflammation in the cortex. Additionally, convolutional neural networks were used to separate glomeruli from other objects in the cortex. This combined measure of inflammation was then correlated with rescored Banff total inflammation classification and outcomes. RESULTS: Identification of glomeruli on biopsies had high fidelity (mean pixelwise dice coefficient of .858). Continuous total inflammation scores correlated well with Banff rescoring (maximum Pearson correlation .824). A separate set of thresholds resulted in a significant correlation with alloimmune graft loss. CONCLUSIONS: Automated scoring of inflammation showed a high correlation with Banff scoring. Digital image analysis provides a powerful tool for analysis of renal pathology, not only because it is reproducible and can be automated, but also because it provides much more granular data for studies.

Adjunctive use of p16 immunohistochemistry for optimizing management of CIN lesions in a high-risk human papillomavirus-positive population.

INTRODUCTION: Immunostaining with p16INK4a (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.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies.

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.

Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.

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

Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning.

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+ and CD4+GATA3+ cell density (R = 0.74, p < 0.001). Our study demonstrates that CNNs can be used to leverage reliable, quantitative results from mTSA-stained, multi spectrally imaged slides of kidney transplant biopsies.

Developing image analysis pipelines of whole-slide images: Pre- and post-processing.

Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size - typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images.

Deep Learning-Based Histopathologic Assessment of Kidney Tissue.

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.

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images (WSIs) for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 WSIs. The evaluation metric used was a quadratic weighted Cohen's kappa. We discuss the algorithmic details of the 10 best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre- and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy, and pre- and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to -0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targeted for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination.

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset.

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.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images.

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.

Evaluation of p16/Ki-67 dual-stained cytology as triage test for high-risk human papillomavirus-positive women.

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 Pap cytology worldwide varies, and differences in sensitivity and specificity are limited between the three selected strategies, p16/Ki-67 triage of all high-risk HPV-positive samples would be the most reliable strategy in triage of high-risk HPV-positive women with an increased specificity and similar sensitivity compared with Pap cytology triage.

Nasopharyngeal carriage of respiratory pathogens in Warao Amerindians: significant relationship with stunting.

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.

3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging.

PURPOSE: In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs. METHODS: To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms. RESULTS: The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT). CONCLUSIONS: Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis.

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Stunting correlates with high salivary and serum antibody levels after 13-valent pneumococcal conjugate vaccination of Venezuelan Amerindian children.

OBJECTIVE: To determine the impact of pre-vaccination nutritional status on vaccine responses in Venezuelan Warao Amerindian children vaccinated with the 13-valent pneumococcal conjugate vaccine (PCV13) and to investigate whether saliva can be used as read-out for these vaccine responses. METHODS: A cross-sectional cohort of 504 Venezuelan Warao children aged 6 weeks - 59 months residing in nine geographically isolated Warao communities were vaccinated with a primary series of PCV13 according to Centers for Disease Control and Prevention (CDC)-recommended age-related schedules. Post-vaccination antibody concentrations in serum and saliva of 411 children were measured by multiplex immunoassay. The influence of malnutrition present upon vaccination on post-vaccination antibody levels was assessed by univariate and multivariable generalized estimating equations linear regression analysis. RESULTS: In both stunted (38%) and non-stunted (62%) children, salivary antibody concentrations correlated well with serum levels for all serotypes with coefficients varying from 0.61 for serotype 3-0.80 for serotypes 5, 6A and 23F (all p < 0.01). Surprisingly, higher serum and salivary antibody levels were observed with increasing levels of stunting in children for all serotypes. This was statistically significant for 5/13 and 11/13 serotype-specific serum and saliva IgG concentrations respectively. CONCLUSION: Stunted Amerindian children showed generally higher antibody concentrations than well-nourished children following PCV13 vaccination, indicating that chronic malnutrition influences vaccine response. Saliva samples might be useful to monitor serotype-specific antibody levels induced by PCV vaccination. This would greatly facilitate studies of vaccine efficacy in rural settings, since participant resistance generally hampers blood drawing.