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Immunotherapy deals with therapeutic interventions to arrest the progression of tumours using the immune system. These include checkpoint inhibitors, T-cell manipulation, cytokines, oncolytic viruses and tumour vaccines. In this paper, we present a survey of the latest developments on immunotherapy in colorectal cancer (CRC) and the role of artificial intelligence (AI) in this context. Among these, microsatellite instability (MSI) is perhaps the most popular IO biomarker globally. We first discuss the MSI status of tumours, its implications for patient management, and its relationship to immune response. In recent years, several aspiring studies have used AI to predict the MSI status of patients from digital whole-slide images (WSIs) of routine diagnostic slides. We present a survey of AI literature on the prediction of MSI and tumour mutation burden from digitised WSIs of haematoxylin and eosin-stained diagnostic slides. We discuss AI approaches in detail and elaborate their contributions, limitations and key takeaways to drive future research. We further expand this survey to other IO-related biomarkers like immune cell infiltrates and alternate data modalities like immunohistochemistry and gene expression. Finally, we underline possible future directions in immunotherapy for CRC and promise of AI to accelerate this exploration for patient benefits.

Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1–70%. In most cases (>75%), the missing tissue area size was p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.

Background:\ud Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests.\ud Methods:\ud In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods.\ud Findings:\ud Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation.\ud Interpretation:\ud After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches.\ud Funding:\ud The UK Medical Research Council.

BackgroundHistopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. With the aims of facilitating diagnostic decision-making and improving the use of pathologists’ time, we developed an AI-based pre-screening tool that analyses whole slide images (WSIs) of large bowel biopsies to identify normal, inflammatory, and neoplastic biopsies.MethodsTo learn the differential histological patterns from digitised WSIs of large bowel biopsy slides stained with Haematoxylin and Eosin (H&E), our proposed weakly supervised deep learning method uses only slide-level diagnostic labels and no detailed cell or region-level annotations. The proposed method was developed on an internal cohort of biopsy slides (n=5054) from a single laboratory labelled with corresponding diagnostic categories assigned by pathologists. Performance of the tool was evaluated on the internal development cohort (n=5054) in a cross-validation setting, and three external unseen cohorts (n=1536) for independent validation.FindingsThe proposed tool demonstrates high degree of accuracy to assist with the pre-screening of large bowel biopsies, being able to identify neoplastic biopsies (AUROC = 0·993), inflammatory biopsies (AUROC = 0·966) and all abnormal biopsies (AUROC = 0·979). On the three independent validation cohorts, it achieves AUROC values of 0·943, 0·958 and 0·964 for the detection of abnormal biopsies. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant histological features. Interestingly, after examining diagnostic discrepancies between the proposed AI tool and original diagnostic labels, a panel of pathologists found that the proposed tool correctly identified a number of abnormal slides that had been initially reported as normal.InterpretationsThe proposed tool with its high sensitivity of detecting abnormal colorectal biopsies promises significant improvements in clinical workflow efficiency and assistance in diagnostic decision-making through pre-screening of normal biopsies.FundingInnovate UK on behalf of UK Research and Innovation.

ObjectivesDevelop an interpretable AI algorithm to rule out normal large bowel endoscopic biopsies saving pathologist resources.DesignRetrospective study.SettingOne UK NHS site was used for model training and internal validation. External validation conducted on data from two other NHS sites and one site in Portugal.Participants6,591 whole-slides images of endoscopic large bowel biopsies from 3,291 patients (54% Female, 46% Male).Main outcome measuresArea under the receiver operating characteristic and precision recall curves (AUC-ROC and AUC-PR), measuring agreement between consensus pathologist diagnosis and AI generated classification of normal versus abnormal biopsies.ResultsA graph neural network was developed incorporating pathologist domain knowledge to classify the biopsies as normal or abnormal using clinically driven interpretable features. Model training and internal validation were performed on 5,054 whole slide images of 2,080 patients from a single NHS site resulting in an AUC-ROC of 0.98 (SD=0.004) and AUC-PR of 0.98 (SD=0.003). The predictive performance of the model was consistent in testing over 1,537 whole slide images of 1,211 patients from three independent external datasets with mean AUC-ROC = 0.97 (SD=0.007) and AUC-PR = 0.97 (SD=0.005). Our analysis shows that at a high sensitivity threshold of 99%, the proposed model can, on average, reduce the number of normal slides to be reviewed by a pathologist by 55%. A key advantage of IGUANA is its ability to provide an explainable output highlighting potential abnormalities in a whole slide image as a heatmap overlay in addition to numerical values associating model prediction with various histological features. Example results with can be viewed online athttps://iguana.dcs.warwick.ac.uk/.ConclusionsAn interpretable AI model was developed to screen abnormal cases for review by pathologists. The model achieved consistently high predictive accuracy on independent cohorts showing its potential in optimising increasingly scarce pathologist resources and for achieving faster time to diagnosis. Explainable predictions of IGUANA can guide pathologists in their diagnostic decision making and help boost their confidence in the algorithm, paving the way for future clinical adoption.What is already known on this topicIncreasing screening rates for early detection of colon cancer are placing significant pressure on already understaffed and overloaded histopathology resources worldwide and especially in the United Kingdom1.Approximately a third of endoscopic colon biopsies are reported as normal and therefore require minimal intervention, yet the biopsy results can take up to 2-3 weeks2.AI models hold great promise for reducing the burden of diagnostics for cancer screening but require incorporation of pathologist domain knowledge and explainability.What this study addsThis study presents the first AI algorithm for rule out of normal from abnormal large bowel endoscopic biopsies with high accuracy across different patient populations.For colon biopsies predicted as abnormal, the model can highlight diagnostically important biopsy regions and provide a list of clinically meaningful features of those regions such as glandular architecture, inflammatory cell density and spatial relationships between inflammatory cells, glandular structures and the epithelium.The proposed tool can both screen out normal biopsies and act as a decision support tool for abnormal biopsies, therefore offering a significant reduction in the pathologist workload and faster turnaround times.

Access to cyclopropanol is improved by developing a route from highly available, cyclopropyl methyl ketone. The Baeyer-Villiger oxidation inserts oxygen between the cyclopropyl and ketone functionalities, and the alcohol is subsequently unmasked by cleaving the ester with an amine. Optimization resulted in high yield (>90% for each step), isolation of volatile and water soluble cylopropanol, selection of a peroxide with improved safety profile (urea hydrogen peroxide, UHP), and implementation of continuous flow to handle an exotherm. The urea present in the peroxide unexpectedly enhanced conversion of the ketone to the ester, and the addition of a rheology modifier to a 20 wt% suspension of UHP in DCM facilitated continuous delivery of an otherwise difficult to pump slurry.

A high-yielding protocol for atropisomeric resolution was developed by rectifying inherent incompatibilities between crystallization and epimerization via continuous processing. Application toward synthesis of MRTX1719, a densely functionalized active pharmaceutical ingredient (API), improved yield from 37% to 87%. This protocol provides a complementary means to access rotamers which challenge current asymmetric methodologies, and greatly improves sustainability by decreasing consumption of solvent and advanced synthetic intermediates.

ObjectiveTo develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.DesignA graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site.ResultsModel training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features.ConclusionThe model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption.

PURPOSE: The introduction of whole slide imaging and digital pathology has enabled greater scrutiny of visual search behaviors among pathologists. We aim to investigate zooming and panning behaviors, external markers of visual processing capabilities, and the changes with experience. APPROACHES: Twenty digitized breast core needle biopsy histopathology slides were obtained from the circulating slides from the main digital pathology trial (IRAS number: 258799). These were presented to five pathologists with varying experience (1.5 to 40 years) whose examinations were recorded. Data of visual fixations were collected using eye-tracking cameras, and the magnification data and zooming behaviors were extracted in an objective fashion by an automated algorithm. The relationship between experience and metrics was analyzed using mixed-effects regression analyses. RESULTS: There was a significant association between experience and both reading times ([Formula: see text]) and a number of fixations ([Formula: see text]), with these relationships being inversely proportional. The greater experience was also associated with greater diagnostic accuracy ([Formula: see text]). We found that experience was significantly associated with greater use of magnification changes ([Formula: see text]). Conversely, less experience showed a near significant association with the increased proportion of time spent panning ([Formula: see text]). CONCLUSIONS: Fewer fixations needed to reach a diagnosis and quicker reading times are indicative of greater cognitive and visual processing capabilities with greater experience. These cognitive capabilities may be a prerequisite for the more frequent zooming changes that are more prevalent with increasing experience.

Bedaquiline is a crucial medicine in the global fight against tuberculosis, yet its high price places it out of reach for many patients. Herein, we describe improvements to the key industrial lithiation-addition sequence that enable a higher yielding and therefore more economical synthesis of bedaquiline. Prioritization of mechanistic understanding and multi-lab reproducibility led to optimized reaction conditions that feature an unusual base–salt pairing and afford a doubling of the yield of racemic bedaquiline. We anticipate that implementation of these improvements on manufacturing scale will be facile, thereby substantially increasing the accessibility of this essential medication.

Lung adenocarcinoma (LUAD) tumour tissue grows into variable morphological architecture called growth patterns (GPs). The GPs are clinically linked to the biological behaviour of the tumour. However, due to the complex heterogeneity of the tumours, there is high inter-and intra-observer variability in the pathologist reporting of GPs. In this paper, we propose a deep learning model for the automatic classification of the LUAD growth patterns in whole slide images (WSIs). The model is trained and tested on 78 cases of LUAD in the digitised WSI of the sample. For each case, all the growth patterns were automatically classified and quantified. Our multivariate analysis shows that lepidic and the micropapillary patterns are independent predictors for five-year survival (p

Leucine-rich repeat kinase 2 (LRRK2) is the most commonly mutated gene in familial Parkinson's disease1 and is also linked to its idiopathic form2. LRRK2 has been proposed to function in membrane trafficking3 and colocalizes with microtubules4. Despite the fundamental importance of LRRK2 for understanding and treating Parkinson's disease, structural information on the enzyme is limited. Here we report the structure of the catalytic half of LRRK2, and an atomic model of microtubule-associated LRRK2 built using a reported cryo-electron tomography in situ structure5. We propose that the conformation of the LRRK2 kinase domain regulates its interactions with microtubules, with a closed conformation favouring oligomerization on microtubules. We show that the catalytic half of LRRK2 is sufficient for filament formation and blocks the motility of the microtubule-based motors kinesin 1 and cytoplasmic dynein 1 in vitro. Kinase inhibitors that stabilize an open conformation relieve this interference and reduce the formation of LRRK2 filaments in cells, whereas inhibitors that stabilize a closed conformation do not. Our findings suggest that LRRK2 can act as a roadblock for microtubule-based motors and have implications for the design of therapeutic LRRK2 kinase inhibitors.

The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.

Aim The rate of deployment of digital pathology (DP) systems for primary diagnosis in the UK is accelerating. The flexibility and resilience of digital versus standard glass slides could be of great benefit in the NHS breast screening programme (NHSBSP). This study aims to document the safety and benefits of DP for preoperative tissue diagnosis of screen-detected breast lesions. Methods and results Concordance data for glass and digital slides of the same cases from four sites were subjected to detailed concordance-discordance analysis. A literature review of DP in the primary diagnosis of breast lesions is presented, making this the most comprehensive synthesis of digital breast cancer histopathological diagnostic data to date. Detailed concordance analysis of experimental data from two histopathology departments reveals clinical concordance rates for breast biopsies of 96% (216 of 225) and 99.6% (249 of 250). Data from direct comparison validation studies in two histopathology departments, utilising the protocol recommended by the Royal College of Pathologists, found concordance rates for breast histology cases of 99.4% (180 of 181) and 99.0% (887 of 896). An intraobserver variation study for glass versus digital slides for difficult cases from the NHSBSP yielded a kappa statistic of 0.80, indicating excellent agreement. Discordances encountered in the studies most frequently concerned discrepancies in grading attributable to mitotic count-scoring and identification of weddelite. Conclusions The experience of four histopathology laboratories and our review of pre-existing literature suggests that DP is safe for the primary diagnosis of NHSBSP breast histology specimens, and does not increase the risk of misclassification.

Introduction: The VENTANA PD-L1 (SP263) Assay is approved for use with anti–programmed cell death-1/programmed cell death ligand-1 (PD-1/PD-L1) therapies in NSCLC and urothelial carcinoma. Here, we investigate interobserver reliability of the SP263 assay, applied to PD-L1 scoring of tumor cells (TCs) in NSCLC. Methods: Six practicing European pulmonary pathologists independently scored the proportion of TCs expressing PD-L1 (TC score) from 200 archival, commercially sourced, formalin-fixed paraffin-embedded NSCLC resections stained using the SP263 assay. Agreement in scores was analyzed using the intraclass correlation coefficient and concordance in patient's classification using Fleiss’ kappa. Results: Results from 172 samples showed strong pair-wise correlations between pathologists (R2 >0.89) for TC scoring with an intraclass correlation coefficient of 0.96. Overall agreement was greater than 90% for TC of 1% and above, and greater than 94% for TCs of at least 25% and at least 50%. Fleiss’ kappa showed substantial agreement for TC of 1% and above, and almost perfect agreement for TCs of at least 25% and at least 50%. Conclusions: Assessment of TC score in NSCLC was highly reproducible using the SP263 assay, building confidence in the accuracy of this assay in selection of patients for anti–PD-1/PD-L1 therapy.

Histopathological examination is a pivotal step in the diagnosis and treatment planning of many major diseases. To facilitate the diagnostic decision-making and reduce the workload of pathologists, we present an AI-based pre-screening tool capable of identifying normal and neoplastic colon biopsies. To learn the differential histological patterns from whole slides images (WSIs) stained with hematoxylin and eosin (H&E), our proposed weakly supervised deep learning method requires only slide-level labels and no detailed cell or region-level annotations. The proposed method was developed and validated on an internal cohort of biopsy slides (n=4 292) from two hospitals labeled with corresponding diagnostic categories assigned by pathologists after reviewing case reports. Performance of the proposed colon cancer pre-screening tool was evaluated in a cross-validation setting using the internal cohort (n=4 292) and also by an external validation on The Cancer Genome Atlas (TCGA) cohort (n=731). With overall cross-validated classification accuracy (AUROC = 0.9895) and external validation accuracy (AUROC = 0.9746), the proposed tool promises high accuracy to assist with the pre-screening of colorectal biopsies in clinical practice. Analysis of saliency maps confirms the representation of disease heterogeneity in model predictions and their association with relevant pathological features. The proposed AI tool correctly reported some slides as neoplastic while clinical reports suggested they were normal. Additionally, we analyzed genetic mutations and gene enrichment analysis of AI-generated neoplastic scores to gain further insight into the model predictions and explore the association between neoplastic histology and genetic heterogeneity through representative genes and signaling pathways.

The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a single task and therefore scale poorly as we wish to adapt the model for an increasing number of different tasks. Also, supervised deep learning models are very data hungry and therefore rely on large amounts of training data to perform well. In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources. While ensuring that our tasks are aligned by the same tissue type and resolution, we enable meaningful simultaneous prediction with a single network. As a result of feature sharing, we also show that the learned representation can be used to improve the performance of additional tasks via transfer learning, including nuclear classification and signet ring cell detection. As part of this work, we train our developed Cerberus model on a huge amount of data, consisting of over 600 thousand objects for segmentation and 440 thousand patches for classification. We use our approach to process 599 colorectal whole-slide images from TCGA, where we localise 377 million, 900 thousand and 2.1 million nuclei, glands and lumina respectively. We make this resource available to remove a major barrier in the development of explainable models for computational pathology.

There are very few published cases of total anterior staphyloma, all of which have been reported as secondary to fungal keratitis. This study reports the clinical and histopathological findings and subsequent management of a 27-year-old healthy female patient who developed total anterior staphyloma after poor compliance with treatment for clinically diagnosed acanthamoeba keratitis. She underwent a successful evisceration with good long-term results. This case highlights that total anterior staphyloma may also result from untreated keratitis which is not fungal in origin. In cases of fungal and acanthamoeba keratitis, patient compliance with both treatment and follow-up is paramount to avoid vision-threatening sequelae that present significant challenges in their management.

The development of deep segmentation models for computational pathology (CPath) can help foster the investigation of interpretable morphological biomarkers. Yet, there is a major bottleneck in the success of such approaches because supervised deep learning models require an abundance of accurately labelled data. This issue is exacerbated in the field of CPath because the generation of detailed annotations usually demands the input of a pathologist to be able to distinguish between different tissue constructs and nuclei. Manually labelling nuclei may not be a feasible approach for collecting large-scale annotated datasets, especially when a single image region can contain thousands of different cells. However, solely relying on automatic generation of annotations will limit the accuracy and reliability of ground truth. Therefore, to help overcome the above challenges, we propose a multi-stage annotation pipeline to enable the collection of large-scale datasets for histology image analysis, with pathologist-in-the-loop refinement steps. Using this pipeline, we generate the largest known nuclear instance segmentation and classification dataset, containing nearly half a million labelled nuclei in H&E stained colon tissue. We have released the dataset and encourage the research community to utilise it to drive forward the development of downstream cell-based models in CPath.

We present an asthmatic 39 year old female frontline healthcare worker in contact with COVID-19 patients, who suffered from respiratory problems for a few days, after which she was found dead by her spouse. Post-mortem (PM) examination showed lung consolidation and histological evidence of diffuse alveolar damage (DAD), in form of hyaline membrane disease, as well as atypical cells, thrombi and fibrin plugs inside pulmonary capillaries. Swab sample testing for SARS-CoV-2 confirmed the infection status. SARS-CoV-2 is a novel coronavirus which first emerged in Wuhan, China, and PM is contributing immensely to uncovering the pathophysiological mechanism of this disease. SARS-CoV-2 enters host cells via the angiotensin-converting enzyme 2 (ACE2), and can lead to a fatal pneumonia characterised histologically by DAD (the most consistent PM finding). This is due to invasion by the virus of type II pneumocytes, which leads to the death of both type I and II pneumocytes and increased capillary permeability. This compromises gas exchange which can lead eventually to cardiorespiratory failure and death.

BackgroundDigital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence–based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature.ObjectivesWe aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process.MethodsA comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies.ResultsTwenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%).ConclusionThe results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP.

4576 Background: Bladder Urothelial Carcinoma (BLCA) is the most common type of bladder cancer and the sixth most frequent cancer in the US. High numbers of lymphocytes colocated with tumour-associated stromal (TAS) tissue has demonstrated prognostic significance for overall survival in multiple cancers. Our goal in this study was to explore the prognostic significance of an automated score to quantify the colocalisation of lymphocytes and TAS for overall survival (OS) in BLCA patients from Haematoxylin & Eosin (H&E) stained Whole Slide Images (WSIs). Methods: Two cohorts of BLCA patients were included in this study. From the UK, a cohort of 67 BLCA patients constituted cohort A. We also evaluated our method on The Cancer Genome Atlas (TCGA) bladder cancer cohort of 453 cases, which we refer to as cohort B. We developed a two-stage method for digital quantification of lymphocytic infiltrates in TAS. First, we employed an AI algorithm to recognise and classify different regions as areas of high concentration of tumour, lymphocytes and stroma for each WSI creating a segmentation map of the different tissue types. For patients with multiple WSIs, we used the slide with the highest percentage of tumour to predict survival. This algorithm had been pre-trained on a cohort of Oral cancer and was fine-tuned using annotations from 4 BLCA WSIs, 3 from cohort A and 1 from cohort B. These WSIs were excluded from the final survival analysis. Using the segmentation maps, a statistical measure for the colocalisation of TAS and lymphocytes termed the tumour-associated stroma infiltrating lymphocytes or (TASIL) score was computed. Finally, for each cohort, data was right-censored at 10 years and the digital BLCA-TASIL (BT) score’s prognostic significance for OS was investigated by fitting a Kaplan-Meier estimator and Cox proportional hazard (PH) analysis, stratifying patients into two groups based on the BT score. In each cohort, two thirds of the data was used as the discovery set to determine the best cut-off for the TASIL Score and the remaining third was used as the validation set. Results: Our classification algorithm achieved high average F1-score of 0.88 on a held-out set of unseen data for the classification of tumour, lymphocytic and stromal regions. In cohort A, higher BT score was strongly associated with better OS ( P= 0.00906) on the unseen validation data. This significant association was also found in the validation data of cohort B ( P< 0.001). Using the BT score as the only covariate, a Cox PH model for the validation data resulted in a C-index of 0.73 for cohort A and 0.57 for cohort B, respectively. Conclusions: The digital BT score showed significant prognostic value for overall survival in both BLCA cohorts, reinforcing the findings of prior work. We intend to further validate these findings on another cohort. To the best of our knowledge, this is the first attempt to predict survival solely from H&E slides in BLCA.

The National Health Service (NHS) in the United Kingdom (UK) is on the verge of transitioning into the use of digital pathology. Technological advancements such as whole slide imaging (WSI), whereby microscopy slides are scanned using a high throughput device to produce digital copies of the slides, has enabled such revolution possible. These digital images enable viewing using image viewer systems on computer monitors, hence making eye tracking studies feasible. The Digital Pathology trial has been set up to evaluate the use of virtual microscopy in the UK. As part of this trial, we conducted two pilot studies as our initial evaluation prior to larger scale studies. In this study, we present the initial findings from our latest pilot study which aims to characterise visual search patterns in pathologists of varying experience. This study was conducted with three pathologists at different stages of their careers, looking at core needle biopsy of breast specimens. We found that the more experienced pathologist had shorter reading duration and had lesser number of fixations consistent with our previous pilot study findings. However, this study did not reproduce any significant finding on magnification pattern. Further work would require a larger sample size to reach more certainty.

SummaryBackgroundDetermining molecular pathways involved in the development of colorectal cancer (CRC) and knowing the status of key mutations are crucial for deciding optimal target therapy. The goal of this study is to explore machine learning to predict the status of the three main CRC molecular pathways – microsatellite instability (MSI), chromosomal instability (CIN), CpG island methylator phenotype (CIMP) – and to detect BRAF and TP53 mutations as well as to predict hypermutated (HM) CRC tumors from whole-slide images (WSIs) of colorectal cancer (CRC) slides stained with Hematoxylin and Eosin (H&E).MethodsWe propose a novel iterative draw-and-rank sampling (IDaRS) algorithm to select representative sub-images or tiles from a WSI given a single WSI-level label, without needing any detailed annotations at the cell or region levels. IDaRS is used to train a deep convolutional network for predicting key molecular parameters in CRC (in particular, prediction of HM tumors and the status of three main CRC molecular pathways – MSI, CIN, CIMP – as well as the detection of two key mutations, BRAF and TP53) from digitized images of routine H&E stained tissue slides of CRC patients (n=497 for TCGA cohort and n=47 cases for the Pathology AI Platform or PAIP cohort). Visual fields most predictive of each pathway and HM tumors identified by IDaRS are analyzed for verification of known histological features for the first time to reveal novel histological features. This is achieved by systematic, data-driven analysis of the cellular composition of strongly predictive tiles.FindingsIDaRS yields high prediction accuracy for prediction of the three main CRC genetic pathways and key mutations by deep learning based analysis of the WSIs of H&E stained slides. It achieves the state-of-the-art AUROC values of 0.90, 0.83, and 0.81 for prediction of the status of MSI, CIN, and HM tumors for the TCGA cohort, which is significantly higher than any other currently published methods on that cohort. We also report prediction of status of CIMP pathway (CIMP-High and CIMP-Low) from H&E slides, with an AUROC of 0.79. We analyzed key discriminative histological features associated with HM tumors and each molecular pathway in a data-driven manner, via an automated quantitative analysis of the cellular composition of tiles strongly predictive of the corresponding molecular status. A key feature of the proposed method is that it enables a systematic and data-driven analysis of the cellular composition of image tiles strongly predictive of the various molecular parameters. We found that relatively high proportion of tumor infiltrating lymphocytes and necrosis are found to be strongly associated with HM and MSI, and moderately associated with CIMP-H and genome-stable (GS) cases, whereas relatively high proportions of neoplastic epithelial type 2 (NEP2), mesenchymal and neoplastic epithelial type 1 (NEP1) cells are found to be associated with CIN cases.InterpretationAutomated prediction of genetic pathways and key mutations from image analysis of simple H&E stained sections with a high accuracy can provide time and cost-effective decision support. This work shows that a deep learning algorithm can mine both visually recognizable as well as sub-visual histological patterns associated with molecular pathways and key mutations in CRC in a data-driven manner.FundingThis study was funded by the UK Medical Research Council (award MR/P015476/1).

Recent advances in whole‐slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well‐defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large‐scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real‐world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping.\ud \ud

We demonstrate a continuous two-step sequence where a sulfenyl chloride is formed, trapped by vinyl acetate and chlorinated further via a Pummerer rearrangement. This sequence produces a key intermediate in our new approach to the oxathiolane core used to prepare the anti-retroviral medicines Emtricitabine and Lamivudine. During batch scale-up to tens of grams, we found that the sequence featured a strong exotherm, temperature and pressure sensitivity, and evolution of hydrogen chloride and sulfur dioxide. These reactions are ideal candidates for implementation in a continuous, mesoscale system for the sake of superior control. In addition, we found that fast reagent additions at controlled temperatures decreased byproduct formation. Herein, we discuss the flow implementation and the final reactor design that led to a 141g/h throughput system.

Pathology departments must rise to new staffing challenges caused by the coronavirus disease-19 pandemic and may need to work more flexibly for the foreseeable future. In light of this, many pathologists and departments are considering the merits of remote or home reporting of digital cases. While some individuals have experience of this, little work has been done to determine optimum conditions for home reporting, including technical and training considerations. In this publication produced in response to the pandemic, we provide information regarding risk assessment of home reporting of digital slides, summarize available information on specifications for home reporting computing equipment, and share access to a novel point-of-use quality assurance tool for assessing the suitability of home reporting screens for digital slide diagnosis. We hope this study provides a useful starting point and some practical guidance in a difficult time. This study forms the basis of the guidance issued by the Royal College of Pathologists, available at: https://www.rcpath.org/uploads/assets/626ead77-d7dd-42e1-949988e43dc84c97/RCPath-guidance-for-remote-digital-pathology.pdf.

Pathology in the UK is on the verge of transformation from analogue to digital practice through the development of digital pathology (DP). Advances in technology has allowed for this change to occur through the use of high-throughput slide scanners to obtain whole histopathology glass slides onto computer workstations rather than the use of a conventional light microscope (LM). Previous studies have shown that the use of digital imaging to view histopathology slides has proven to be of benefit to pathology departments. It allows pathologists to analyse samples remote from the laboratory, making sharing of the slides between pathologists more straight-forward, and also enables expert review out of hours. With the ability to electronically transfer slides from the laboratory to the reporting pathologist, it may provide solutions for local shortages of pathologists across NHS trusts in the UK. However, a number of researchers argue that the costs of implementing digital pathology may outweigh its advantages. Moreover, images produced by DP systems are often of inferior resolution when compared to conventional light microscopy. The lack of literature on this subject limits the adoption of this new technology by laboratories across the country. This multi-centre study aims to analyse how the study pathologists examine DP images of different pathology modalities by using eye-tracking technology, thus using data on their reading and interpretation technique to improve performance and contribute to the adoption of DP across the UK.

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. $224\times 224$ ) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of $1792\times 1792$ pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method.

Leucine Rich Repeat Kinase 2 (LRRK2) is the most commonly mutated gene in familial Parkinson’s disease. LRRK2 is proposed to function in membrane trafficking and co-localizes with microtubules. We report the 3.5Å structure of the catalytic half of LRRK2, and an atomic model of microtubule-associated LRRK2 built using a reported 14Å cryo-electron tomography in situ structure. We propose that the conformation of LRRK2’s kinase domain regulates its microtubule interaction, with a closed conformation favoring binding. We show that the catalytic half of LRRK2 is sufficient for microtubule binding and blocks the motility of the microtubule-based motors kinesin and dynein in vitro. Kinase inhibitors that stabilize an open conformation relieve this interference and reduce LRRK2 filament formation in cells, while those that stabilize a closed conformation do not. Our findings suggest that LRRK2 is a roadblock for microtubule-based motors and have implications for the design of therapeutic LRRK2 kinase inhibitors.

One of the challenges in the current COVID-19 crisis is the time and cost of performing tests especially for large-scale population surveillance. Since, the probability of testing positive in large population studies is expected to be small (https://github.com/foxtrotmike/AS.

Pemphigus vulgaris (PV) is a rare autoimmune bullous disease which affects the skin and mucous membranes. Oesophageal involvement is rare and has previously been limited to case reports and case series. A recent large case series of 477 PV patients showed that 26/477 (5.4%) had symptomatic oesophageal involvement. We present the case of a 54-year-old Somalian lady with a 10-year history of cutaneous PV, currently in remission, who developed dysphagia and odynophagia and was subsequently found to have oesophageal PV involvement with multiple flaccid bullae which were positive for anti-DSG3 antibodies on in-direct immunofluorescence. She had her treatment switched from azathioprine to mycophenolate and prednisolone, leading to resolution of her symptoms.

Determining the grade of colon cancer from tissue slides is a routine part of the pathological analysis. In the case of colorectal adenocarcinoma (CRA), grading is partly determined by morphology and degree of formation of glandular structures. Achieving consistency between pathologists is difficult due to the subjective nature of grading assessment. An objective grading using computer algorithms will be more consistent, and will be able to analyse images in more detail. In this paper, we measure the shape of glands with a novel metric that we call the Best Alignment Metric (BAM). We show a strong correlation between a novel measure of glandular shape and grade of the tumour. We used shape specific parameters to perform a two-class classification of images into normal or cancerous tissue and a three-class classification into normal, low grade cancer, and high grade cancer. The task of detecting gland boundaries, which is a prerequisite of shape-based analysis, was carried out using a deep convolutional neural network designed for segmentation of glandular structures. A support vector machine (SVM) classifier was trained using shape features derived from BAM. Through cross-validation, we achieved an accuracy of 97% for the two-class and 91% for three-class classification. 1 2017 The Author(s). This work was made possible by NPRP grant number NPRP5-1345-1-228 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The authors are grateful to Dr. Yee-Wah Tsang from the Department of Pathology, UHCW for her assistance in annotating gland boundaries. Scopus