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We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients. [ABSTRACT FROM AUTHOR]

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed. [ABSTRACT FROM AUTHOR]

Background and objective: Training a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case reports can be used to enrich the training dataset. However, the magnifications of these invaluable snapshots are generally unknown, which limits their usage. Therefore, a robust magnification predictor is required for utilizing those diverse snapshot repositories consisting of different diseases. This paper presents a magnification prediction model named Hagnifinder for H&E-stained histological images. Methods: Hagnifinder is a regression model based on a modified convolutional neural network (CNN) that contains 3 modules: Feature Extraction Module, Regression Module, and Adaptive Scaling Module (ASM). In the training phase, the Feature Extraction Module first extracts the image features. Secondly, the ASM is proposed to address the learned feature values uneven distribution problem. Finally, the Regression Module estimates the mapping between the regularized extracted features and the magnifications. We construct a new dataset for training a robust model, named Hagni40, consisting of 94 643 H&E-stained histology image patches at 40 different magnifications of 13 types of cancer based on The Cancer Genome Atlas. To verify the performance of the Hagnifinder, we measure the accuracy of the predictions by setting the maximum allowable difference values (0.5, 1, and 5) between the predicted magnification and the actual magnification. We compare Hagnifinder with state-of-the-art methods on a public dataset BreakHis and the Hagni40. Results: The Hagnifinder provides consistent prediction accuracy, with a mean accuracy of 98.9%, across 40 different magnifications and 13 different cancer types when Resnet50 is used as the feature extractor. Compared with the stateof-the-art methods focusing on 4-5 levels of magnification classification, the Hagnifinder achieves the best and most comparable performance in the BreakHis and Hagni40 datasets. Conclusions: The experimental results suggest that Hagnifinder can be a valuable tool for predicting the associated magnification of any given histology image. [ABSTRACT FROM AUTHOR]

In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges. [ABSTRACT FROM AUTHOR]

Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products. [ABSTRACT FROM AUTHOR]

Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a K-means algorithm and the resultant partition, defined by the cluster centroids, is herein named the noiselet code-book. A part of an image, a tile, is divided into patches and represented by the histogram of occurrences of the projected patches in the noiselet code-book. Finally, with these histograms, a classifier learns to differentiate between nuclei and non-nuclei tiles. By applying a conventional watershed-marked method to detect and segment nuclei, evaluation consisted in comparing pure watershed method against denoising-plus-watershed in an open database with 8 different types of tissues. The averaged F-score of nuclei detection improved from 0.830 to 0.86 and the dice score after segmentation increased from 0.701 to 0.723. [ABSTRACT FROM AUTHOR]

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices. Material and methods: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology. Results: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data. Discussion: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries. [ABSTRACT FROM AUTHOR]

The cell cycle is a rich field for research, especially, the DNA damage. DNA damage, which happened naturally or as a result of environmental influences causes change in the chemical structure of DNA. The extent of DNA damage has a significant impact on the fate of the cell in later stages. In this paper, we introduced an Unsupervised Machine learning Model for DNA Damage Diagnosis and Analysis. Mainly, we employed K-means clustering unsupervised machine learning algorithms. Unsupervised algorithms commonly draw conclusions from datasets by solely utilizing input vectors, disregarding any known or labeled outcomes. The model provided deep insight about DNA damage and exposes the protein levels for proteins when work together in sub-network model to deal with DNA damage occurrence, the unsupervised artificial model explained the sub-network biological model activities in regard to the changing in their concentrations in several clusters, they have been grouped in such as (0 - no damage, 1 - low, 2 - medium, 3 - high, and 4 - excess) DNA damage clusters. The results provided a rational and persuasive explanation for numerous important phenomena, including the oscillation of the protein p53, in a clear and understandable manner. Which is encouraging since it demonstrates that the K-means clustering approach can be easily applied to many similar biological systems, which aids in better understanding the key dynamics of these systems. [ABSTRACT FROM AUTHOR]

Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems, yet the cost of digital workflows is cited as a major barrier for widespread adoption by many organizations. Memorial Sloan Kettering Cancer Center (MSK) is an early adopter of whole slide imaging with incremental investments in resources that started more than 15 years ago. This experience and the large-scale scan operations led to the identification of required framework components of digital pathology operations. The cost of these components for the 2021 digital pathology operations at MSK were studied and calculated to enable an understanding of the operation and benchmark the accompanying costs. This paper describes the unique infrastructure cost and the costs associated with the digital pathology clinical operation use cases in a large, tertiary cancer center. These calculations can serve as a blueprint for other institutions to provide the necessary concepts and offer insights towards the financial requirements for digital pathology adoption by other institutions. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time. Especially, employing a pretrained segmentation model can further reduce the time than starting annotation from scratch. We trained an accurate ovarian cancer segmentation model with a pretrained breast segmentation model by 3.5 hours of manual annotation which achieved intersection-over-union of 0.74, recall of 0.86, and precision of 0.84. With automatically extracted high-grade serous ovarian cancer patches, we attempted to train an additional classification deep learning model to predict BRCA mutation. [ABSTRACT FROM AUTHOR]

Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations. [ABSTRACT FROM AUTHOR]

Accurate pathologic assessment in placental pathology is mostly dependent on a complete clinical history provided by a clinical team. However, often, the necessary clinical information is lacking, and electronic order sets (EOSs), if implemented correctly, create an opportunity for entering consistent and accurate clinical data. In this viewpoint piece, we describe a framework for synoptic EOS in placental pathology. We outline the necessary data and create optional clinical data that get entered as a dropdown menu of free text. While EOSs are the best way to approach and diagnose placenta and other nonneoplastic pathologic specimens, the barriers for implementation include paper requisitions and a cultural mindset resistance. The aspiration for our synoptic EOS is to become an effective tool for communication between proceduralists and pathologists for proper diagnosis of placental specimens. Through our EOS, the appropriate and complete clinical context is conveyed from the clinical teams to the pathologist. The pathologist can easily and rapidly extract the necessary information to render an accurate and precise diagnosis. The captured data likewise become a valuable research resource. [ABSTRACT FROM AUTHOR]

Requisition storage and retrieval are an integral part of the outpatient laboratory testing process. It is frequently necessary to review an original requisition to confirm the ordering physician, patient demographics, diagnostic information, and requested tests. Manual retrieval of a paper requisition is time-consuming and tedious. Although commercial solutions exist for the scanning and archiving of barcoded paper requisitions, the tools to accomplish this are freely available from the open source software community. We present a simple dedicated piece of software, Reqscan, for scanning patient laboratory requisitions, finding all barcode information, and saving the requisition as a portable document format named according the barcode(s) found. This Python application offers a simple solution to patient requisition digitization. Reqscan has been successfully tested and implemented into routine practice for storage and retrieval of outpatient requisitions at St. Paul's Hospital, Department of Pathology and Laboratory Medicine in Vancouver, British Columbia, Canada. [ABSTRACT FROM AUTHOR]

Historically, most radiology, pathology, and clinical molecular biology information has been image-based and sequestered within each domain. This means that their information could not be combined to improve our knowledge of disease and treatment. This paper proposes the convergence of radiology, pathology, and clinical molecular biology through the integration of their digital data in powerful statistical models in order to create information synergies that can be used by trained models to improve risk estimation, diagnostic certainty, treatment effectiveness, and clinical outcomes. This advance will improve the quality and safety of medical care. Historically, most radiology, pathology, and clinical molecular biology information has been image-based and sequestered within each domain. This means that their information could not be combined to synergistically improve our knowledge of the disease and its treatment. This paper proposes the convergence of radiology, pathology, and clinical molecular biology through the integration of their digital data in a statistical model and the use of the trained model to improve the quality and safety of patient care. During the 20th Century radiology, pathology, and clinical molecular biology were independent clinical domains. Each existed in its own ocular realm - radiologists looked at structural and functional anatomic images, pathologists looked at tissue-based images, and clinical molecular biology looked at biochemical false-color microarray images. Each looked for features that could be used to determine the risk of disease, diagnose disease, and assess the severity of disease. Radiology viewed anatomic images generated by: (i) Röntgen-ray images and (ii) algorithmically constructed images created either from either digital molecular data (for example, magnetic resonance imaging) or from analog cellular data (for example, ultrasound). Pathology viewed enhanced (stains, antibodies, etc.) images of molecular/ cellular/multicellular material affixed to slides. Clinical molecular biology viewed analog false-color images of probedetected gene expression - which could be converted into numeric data, but the resulting numbers were imprecise because of the imprecision inherent in the false-color data. There are several issues related to the visual detection of image-based clinical information. One issue is additivity; each clinical domain possesses some information that is not contained in the other domains (orthogonality), but there was no way to combine the images across the imaging domains in order to create an additive model of the patient and disease. Another issue is observability; there is more information in the analog data than can be seen by an observer, therefore, from a cybernetic perspective, the visual assessment of images results in a loss of information. A third issue is subjectivity; the visual assessment of analog data (signal detection) is subject to high intra and inter-observer variability (error). This lowers predictive accuracy for the three types of prediction, namely, risk/prevention, diagnosis, prognosis/treatment) and, as a consequence, decreases the clinical utility of the information.[1] Fortunately, radiology, pathology, and clinical molecular biology data have become, or are rapidly becoming, digital. This means that the images will no longer be needed - they will be replaced by computational digital data. This transition from analog to digital data permits, for the first time, the convergence of the three domains. The union of these imaging domains will increase the amount, and quality, of clinically useful information through improvements in additivity and observability, and it will reduce error through the elimination of subjectivity. We can employ a conceptual framework, a set of variables and the relations among them that are thought to account for a phenomenon, to help us understand, and guide our modeling of, disease. [2] The body is a unitary, complex biological system[3] which has the following framework: (1) the body is an integrated, interdependent hierarchical organization that is composed of systems, each of which serves one or more biological functions, (2) the complete uncompensated failure of one of the body's necessary systems results in the body's failure - but the body has alternate systems for some functions and they may be able to take over for a failed system; (3) the body can be described in terms of fourdimensional, interconnected levels of analysis, including the molecular, cellular, and multicellular levels; (4) a level is defined in terms of its units and rules (the allowed interactions and activities) and the level's units and rules are the constituents of its functional systems at that level: (5) each level has different units and rules; (6) the levels are interrelated a hierarchical manner; (7) time is different at different levels, i.e., things occur at different rates at different levels, furthermore, body time is not equivalent to level time; (8) complex biological systems are dynamical in that their units are always interacting with each other in order to maintain homeostasis; (9) in terms of the functioning of complex biological systems, the inhibition of an activity is, many times, just as important as the existence of the activity; (10) the body's biological systems self-organize based on their constitutive units and rules; (11) complex biological systems have the ability to adapt to changes in their internal systems, their functions, and their external environment, which means that they evolve over time; (12) complex biological systems have the ability to maintain themselves, protect their existence, and to learn from experience; and (13) the functioning of a complex biological system depends on its present state, its environment, and its feedback and feedforward processes. Finally, higher biological/anatomical levels are constructed from four dimensional multicellular functional units (MFUs). MFUs have at least three characteristics: (1) they are composed of multiple cell types and their local environment, including the extracellular matrix, (2) their cells are co-dependent and spatiotemporally interact in an organized, cooperative manner and (3) they perform one or more biological functions that are required to maintain homeostasis. Biological systems are probabilistic (statistical) rather than deterministic (causal) because they are loosely coupled rather than tightly coupled systems. Tightly coupled means that what the system does in the future is not influenced by what it is currently doing. For example, a spring is governed by the rule of proportionality which means that its response to a stimulus is always proportional to the strength of the stimulus. Because tightly coupled systems are deterministic, they are inflexible, therefore, when conditions change they become maladapted and dysfunctional. Loosely coupled means that the forces affecting the present state of the system can determine, to a greater or lesser degree, the future state of the system. Biological systems are loosely coupled probabilistic systems that can adapt to changes in themselves, their functions, and their environment. For example, in physics, Newton and Einstein created deterministic systems whereas quantum mechanics (a type of statistics) is probabilistic.[4] Complex biological systems are loosely coupled to the extent that they do not jeopardize their existence. A deterministic system does not allow for choice, there is only one outcome, so there is no uncertainty. A probabilistic system always has choice, there is always has more than one possible outcome, so it is inherently uncertain. Since information is anything that reduces uncertainty[5] (uncertainty is mathematically equivalent to Boltzmann entropy) there is no information in a deterministic system. The amount of information in a probabilistic system is a function of its ability to reduce uncertainty. We are interested in the uncertainty related to the accuracy of our risk, diagnosis, and treatment predictions. For example, a patient's biomarker is informative if knowing its numerical value reduces our uncertainty regarding the patient's outcome (e.g., prognosis). Once we have a framework for complex biological systems, we can begin to understand non-traumatic disease. The homeostatic principle is necessary, but not sufficient, for the normal functioning of a biological system. An important homeostatic mechanism is deviation reduction (negative feedback) - which maintains the system's normal functioning. Disease is a biological system that violates the body's homeostatic principle through the use of deviation amplification (positive feedback) - it is this deviation amplification away from homeostasis that allows the disease to cause the body's failure. Disease has many of the characteristics of a chaotic system. Chaotic systems are a special class of loosely coupled systems that employ deviation amplification processes. In some situations, the body is able to contain the disease but, in other situations, the disease takes control of the body and destroys it. Our job is to help the body regain homeostasis through traumatic interventions, for example, surgery, and/or through molecular interventions, for example, medications. To accomplish this task, we must attend to Sir William Osler who said, "Medicine is the science of uncertainty and the art of probability" - in other words, our goal is to reduce uncertainty by creating statistical (probabilistic) models which integrate multilevel biological information in order to create accurate disease representations which help us understand and defeat the disease. It might be thought that the convergence of radiology, pathology, and clinical molecular biology is about the collapsing of three levels of analysis related to anatomic scale - from larger to smaller scale. But our levels of analysis are not related to scale, rather, they are the hierarchical levels related to the molecular, cellular, and multicellular systems and subsystems. These systems and subsystems are fantastically complex. Even the smallest subsystems are extremely complicated, for example, researchers are just beginning to try to model a single signal transduction pathway using supercomputers.[6] Modeling every system in the body is currently not possible, so what is the point of combining orthogonal biological information? The answer is that we are not going to model all the workings of normal biological systems, rather, we are going to model deviant biological systems (disease) - we are going enter the disease-related data into a statistical algorithm, let the statistical method learn the relevant-for-the-disease relationships (rather than all permissible biological system relationships), create a trained model that accurately represents the disease, and use that model of the disease to provide accurate probability estimates related to disease for risk/prevention, diagnosis, and prognosis/ treatment. In other words, we do not need to know all the aspects of a system in order to model those aspects related to the disease and to use that model to improve the quality and safety of patient care. Radiology, pathology, and clinical molecular biology provide the digital molecular, cellular, multicellular data we need to create our disease models. Radiomics is radiographic digital data at the molecular, cellular, multicellular levels.[7] Non-Röntgen-ray radiology is noninvasive and some modalities, such as magnetic resonance imaging, are inherently digital and they can operate at the molecular, cellular, multicellular levels. Pathomics, which is invasive, is the use of statistical algorithms to digitize and learn key features of pathology images and it can operate at the molecular, cellular, multicellular levels. [8] Proteogenomics, which is also invasive, is the acquisition of disease-related digital gene and protein information and, of course, it operates at the molecular, cellular, multicellular levels.[9] Radiomic, pathomics, and proteogenomic digital data can be combined with other digital data, including physiologic and laboratory data, to create an optimal statistical model of the disease. The resulting trained model acts as a surrogate for the disease: to the extent that the model is an accurate representation of the disease, it can inform us regarding the disease's natural course and it can tell us which treatment to select to slow or truncate the disease's progression. In order to select the best statistical method to model the disease we need to formally understand complex biological systems. One way to do so is through network theory, i.e., nodes (functional units) and their connections (the relationships and interactions between nodes). Biological systems can be represented as a multiplex network, i.e., the units at different levels are not separate entities, which means that the data cannot be compressed into a single level model or represented as an aggregated model. Furthermore, we are modeling a dynamical rather than structural network, therefore, a level's units interact with each other and they are constitutive within and across levels (the effects of one level pass through from one level to another), and they can be codependent.[10] Furthermore, we have to be aware of multilevel information redundancy, i.e., that the same information can be shared across levels.[11] Finally, we would like to use as few levels as possible to explain the phenomenon because the more levels there are the sparser the representation. These network characteristics of biological systems have important implications for the convergence of multilevel digital data. One implication is that the statistical method must be able to deal with multilevel, interactional data. Another is that the method must be able to learn from the data. Finally, the method should make few, if any, parametric assumptions. What is required is a universal classifier, one that can learn multilevel dynamical data, including capturing the nonlinearities and interactions, and that will converge on the correct solution which, in this case, is the creation of an accurate model of the disease. Fortunately, there is such a universal classifier, namely, the neural network.[12] A three-layer backpropagation neural network with an arbitrarily large number of sigmoidal hidden layer units can fit any real continuous function and, given that the solution is in the data and that there is sufficient data, it will find the correct solution.[13] The discriminative accuracy of the trained neural network is a function of how well it models the disease and it is measured by the receiver operating characteristic (ROC).[14] To be clinically useful a model's ROC should be at least 0.70.9. Our remaining task is to create clinical decision support systems (CDSS) that: (1) function in the prediction domains of risk/prevention, diagnosis, and prognosis/treatment; (2) contain powerful neural networks that use the patient's digital data to make accurate individualized patient predictions and that learn in order to improve their performance over time; and (3) effectively communicate to the clinician and patient in real time the individual patient clinical information and predictions required for better shared decision-making and optimal treatment. In summary, the transformation of the clinical domains of radiology, pathology, and clinical molecular biology from analog to digital data, and the integration of their digital data in powerful statistical models, will create information synergies that will improve risk estimation, diagnostic certainty, treatment effectiveness, and clinical outcomes. This advance will improve the quality and safety of patient care. [ABSTRACT FROM AUTHOR]

Background: Special consideration should be given when creating and selecting cytopathology specimens for digitization to maximize quality. Advances in scanning and viewing technology can also improve whole-slide imaging (WSI) output quality. Methods: Accumulated laboratory experience with digitization of glass cytopathology slides was collected. Results: This paper describes characteristics of a cytopathology glass slide that can reduce quality on resulting WSI. Important points in the glass cytopathology slide selection process, preparation, scanning, and WSI-editing process that will maximize the quality of the resulting acquired digital image are covered. The paper outlines scanning solutions which have potential to predict issues with a glass cytopathology slide before image acquisition, allowing for adjustment of the scanning approach. WSI viewing solutions that better simulate the traditional microscope experience are also discussed. Conclusion: In addition to taking advantage of technical advances, practical steps can taken to maximize quality of cytopathology WSI. [ABSTRACT FROM AUTHOR]

Integrating the health-care enterprise (IHE) is an international initiative to promote the use of standards to achieve interoperability among health information technology systems. The Pathology and Laboratory Medicine domain within IHE has brought together subject matter experts, electronic health record vendors, and digital imaging vendors, to initiate development of a series of digital pathology interoperability guidelines, called "integration profiles" within IHE. This effort begins with documentation of common use cases, followed by identification of available data and technology standards best utilized to achieve those use cases. An integration profile that describes the information flow and technology interactions is then published for trial use. Real world testing occurs in "connectathon" events, in which multiple vendors attempt to connect their products following the interoperability guidance parameters set forth in the profile. This paper describes the overarching set of integration profiles, one of which has been published, to support key digital pathology use cases. [ABSTRACT FROM AUTHOR]

Background: As a critical technique in a digital pathology laboratory, automatic nuclear detection has been investigated for more than one decade. Conventional methods work on the raw images directly whose color/intensity homogeneity within tissue/cell areas are undermined due to artefacts such as uneven staining, making the subsequent binarization process prone to error. This paper concerns detecting cell nuclei automatically from digital pathology images by enhancing the color homogeneity as a pre processing step. Methods: Unlike previous watershed based algorithms relying on post processing of the watershed, we present a new method that incorporates the staining information of pathological slides in the analysis. This pre processing step strengthens the color homogeneity within the nuclear areas as well as the background areas, while keeping the nuclear edges sharp. Proof of convergence for the proposed algorithm is also provided. After pre processing, Otsu's threshold is applied to binarize the image, which is further segmented via watershed. To keep a proper compromise between removing overlapping and avoiding over segmentation, a naive Bayes classifier is designed to refine the splits suggested by the watershed segmentation. Results: The method is validated with 10 sets of 1000 × 1000 pathology images of lymphoma from one digital slide. The mean precision and recall rates are 87% and 91%, corresponding to a mean F score equal to 89%. Standard deviations for these performance indicators are 5.1%, 1.6% and 3.2% respectively. Conclusion: The precision/recall performance obtained indicates that the proposed method outperforms several other alternatives. In particular, for nuclear detection, stain guided mean shift (SGMS) is more effective than the direct application of mean shift in pre processing. Our experiments also show that pre processing the digital pathology images with SGMS gives better results than conventional watershed algorithms. Nevertheless, as only one type of tissue is tested in this paper, a further study is planned to enhance the robustness of the algorithm so that other types of tissues/stains can also be processed reliably. [ABSTRACT FROM AUTHOR]

Clinicians have traditionally ordered laboratory tests using paper-based orders and requisitions. However, paper orders are becoming increasingly incompatible with the complexities, challenges, and resource constraints of our modern healthcare systems and are being replaced by electronic order entry systems. Electronic systems that allow direct provider input of diagnostic testing or medication orders into a computer system are known as Computerized Provider Order Entry (CPOE) systems. Adoption of laboratory CPOE systems may offer institutions many benefits, including reduced test turnaround time, improved test utilization, and better adherence to practice guidelines. In this review, we outline the functionality of various CPOE implementations, review the reported benefits, and discuss strategies for using CPOE to improve the test ordering process. Further, we discuss barriers to the implementation of CPOE systems that have prevented their more widespread adoption. [ABSTRACT FROM AUTHOR]

Background: The Tissue Microarray Data Exchange Specification (TMA DES) is an eXtensible Markup Language (XML) specification for encoding TMA experiment data in a machine-readable format that is also human readable. TMA DES defines Common Data Elements (CDEs) that form a basic vocabulary for describing TMA data. TMA data are routinely subjected to univariate and multivariate statistical analysis to determine differences or similarities between pathologically distinct groups of tumors for one or more markers or between markers for different groups. Such statistical analysis tests include the t-test, ANOVA, Chi-square, Mann-Whitney U, and Kruskal-Wallis tests. All these generate output that needs to be recorded and stored with TMA data. Materials and Methods: We propose extending the TMA DES to include syntactic and semantic definitions of CDEs for describing the results of statistical analyses performed upon TMA DES data. These CDEs are described in this paper and it is illustrated how they can be added to the TMA DES. We created a Document Type Definition (DTD) file defining the syntax for these CDEs, and a set of ISO 11179 entries providing semantic definitions for them. We describe how we wrote a program in R that read TMA DES data from an XML file, performed statistical analyses on that data, and created a new XML file containing both the original XML data and CDEs representing the results of our analyses. This XML file was submitted to XML parsers in order to confirm that they conformed to the syntax defined in our extended DTD file. TMA DES XML files with deliberately introduced errors were also parsed in order to verify that our new DTD file could perform error checking. Finally, we also validated an existing TMA DES XML file against our DTD file in order to demonstrate the backward compatibility of our DTD. Results: Our experiments demonstrated the encoding of analysis results using our proposed CDEs. We used XML parsers to confirm that these XML data were syntactically correct and conformed to the rules specified in our extended TMA DES DTD. We also demonstrated that this extended DTD was capable of being used to successfully perform error checking, and was backward compatible with pre-existing TMA DES data which did not use our new CDEs. Conclusions: The TMA DES allows Tissue Microarray data to be shared. A variety of statistical tests are used to analyze such data. We have proposed a set of CDEs as an extension to the TMA DES which can be used to annotate TMA DES data with the results of statistical analyses performed on that data. We performed experiments which demonstrated the usage of TMA DES data containing our proposed CDEs. [ABSTRACT FROM AUTHOR]

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine. [ABSTRACT FROM AUTHOR]

Background: Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation. Methods: Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018. Results: Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%). Conclusions: Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM. [ABSTRACT FROM AUTHOR]

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine. [ABSTRACT FROM AUTHOR]

The 13th World Congress on Medical and Health Informatics (Medinfo) was held in 2010 between 12 and 15 September in Cape Town, South Africa. This triennial international gathering is the official conference of the International Medical Informatics Association (IMIA) and brings together leading health informatics leaders, scientists, clinicians, researchers, vendors, developers and government and health care planners from around the globe. The conference attracted 905 submissions and resulted in a program that included 260 oral presentations, 349 posters presentations and 21 scientific demonstrations representing contributions from 58 countries. The Medinfo program covered all aspects of health informatics from traditional areas, such as hospital information systems, patient registries, nursing informatics, data integration, standards, interoperability issues and decision support, to innovative topics, such as translational bioinformatics, text mining, intelligent data analysis, emerging technologies, quality, social networking, workflow and organizational issues. The outgoing President of the IMIA, Professor Reinhold Haux, presented on health informatics challenges into the future, reinforcing that today and in the future, health care has to be considered as part of a continuous and coordinated life-time journey and not just as episodes of disease. Medical informatics has a key role to play in this paradigm shift. The new IMIA President, Professor Antoine Geissbuhler, was announced at the closing ceremony. The next Medinfo congress will take place in Copenhagen, Denmark, in September 2013. [ABSTRACT FROM AUTHOR]

Digital pathology is gaining prominence among the researchers with developments in advanced imaging modalities and new technologies. Generative adversarial networks (GANs) are a recent development in the field of artificial intelligence and since their inception, have boosted considerable interest in digital pathology. GANs and their extensions have opened several ways to tackle many challenging histopathological image processing problems such as color normalization, virtual staining, ink removal, image enhancement, automatic feature extraction, segmentation of nuclei, domain adaptation and data augmentation. This paper reviews recent advances in histopathological image processing using GANs with special emphasis on the future perspectives related to the use of such a technique. The papers included in this review were retrieved by conducting a keyword search on Google Scholar and manually selecting the papers on the subject of H&E stained digital pathology images for histopathological image processing. In the first part, we describe recent literature that use GANs in various image preprocessing tasks such as stain normalization, virtual staining, image enhancement, ink removal, and data augmentation. In the second part, we describe literature that use GANs for image analysis, such as nuclei detection, segmentation, and feature extraction. This review illustrates the role of GANs in digital pathology with the objective to trigger new research on the application of generative models in future research in digital pathology informatics. [ABSTRACT FROM AUTHOR]

Background: Beaker is a relatively new laboratory information system (LIS) offered by Epic Systems Corporation as part of its suite of health-care software and bundled with its electronic medical record, EpicCare. It is divided into two modules, Beaker anatomic pathology (Beaker AP) and Beaker Clinical Pathology. In this report, we describe our experience implementing Beaker AP version 2014 at an academic medical center with a go-live date of October 2015. Methods: This report covers preimplementation preparations and challenges beginning in September 2014, issues discovered soon after go-live in October 2015, and some post go-live optimizations using data from meetings, debriefings, and the project closure document. Results: We share specific issues that we encountered during implementation, including difficulties with the proposed frozen section workflow, developing a shared specimen source dictionary, and implementation of the standard Beaker workflow in large institution with trainees. We share specific strategies that we used to overcome these issues for a successful Beaker AP implementation. Several areas of the laboratory-required adaptation of the default Beaker build parameters to meet the needs of the workflow in a busy academic medical center. In a few areas, our laboratory was unable to use the Beaker functionality to support our workflow, and we have continued to use paper or have altered our workflow. In spite of several difficulties that required creative solutions before go-live, the implementation has been successful based on satisfaction surveys completed by pathologists and others who use the software. However, optimization of Beaker workflows has continued to be an ongoing process after go-live to the present time. Conclusions: The Beaker AP LIS can be successfully implemented at an academic medical center but requires significant forethought, creative adaptation, and continued shared management of the ongoing product by institutional and departmental information technology staff as well as laboratory managers to meet the needs of the laboratory. [ABSTRACT FROM AUTHOR]

Introduction: The registration of histological whole slide images is an important prerequisite for modern histological image analysis. A partial reconstruction of the original volume allows e.g. colocalization analysis of tissue parameters or high-detail reconstructions of anatomical structures in 3D. Methods: In this paper, we present an automatic staining invariant registration method, and as part of that, introduce a novel vessel based rigid registration algorithm using a custom similarity measure. The method is based on an iterative best fit matching of prominent vessel structures. Results: We evaluated our method on a sophisticated synthetic dataset as well as on real histological whole slide images. Based on labeled vessel structures we compared the relative differences for corresponding structures. The average positional error was close to 0, the median for the size change factor was 1, and the median overlap was 0.77. Conclusion: The results show that our approach is very robust and creates high quality reconstructions. The key element for the resulting quality is our novel rigid registration algorithm. [ABSTRACT FROM AUTHOR]

Histological image analysis methods often employ machine-learning classifiers in order to adapt to the huge variability of histological images. To train these classifiers, the user must select samples of the relevant image objects. In the field of active learning, there has been much research on sampling strategies that exploit the uncertainty of the current classification in order to guide the user to maximally informative samples. Although these approaches have the potential to reduce the training effort and increase the classification accuracy, they are very rarely employed in practice. In this paper, we investigate the practical value of uncertainty sampling in the context of histological image analysis. To obtain practically meaningful results, we have devised an evaluation algorithm that simulates the way a human interacts with a user interface . The results show that uncertainty sampling outperforms common random or error sampling strategies by achieving more accurate classification results with a lower number of training images. [ABSTRACT FROM AUTHOR]

In this paper, we attempt to quantify the prognostic information embedded in multi-parametric histologic biopsy images to predict disease aggressiveness in estrogen receptor-positive (ER+) breast cancers (BCa). The novel methodological contribution is in the use of a multi-field-of-view (multi-FOV) framework for integrating image-based information from differently stained histopathology slides. The multi-FOV approach involves a fixed image resolution while simultaneously integrating image descriptors from many FOVs corresponding to different sizes. For each study, the corresponding risk score (high scores reflecting aggressive disease and vice versa), predicted by a molecular assay (Oncotype DX), is available and serves as the surrogate ground truth for long-term patient outcome. Using the risk scores, a trained classifier is used to identify disease aggressiveness for each FOV size. The predictions for each FOV are then combined to yield the final prediction of disease aggressiveness (good, intermediate, or poor outcome). Independent multi-FOV classifiers are constructed for (1) 50 image features describing the spatial arrangement of cancer nuclei (via Voronoi diagram, Delaunay triangulation, and minimum spanning tree graphs) in H and E stained histopathology and (2) one image feature describing the vascular density in CD34 IHC stained histopathology. In a cohort of 29 patients, the multi-FOV classifiers obtained by combining information from the H and E and CD34 IHC stained channels were able to distinguish low- and high-risk patients with an accuracy of 0.91 ± 0.02 and a positive predictive value of 0.94 ± 0.10, suggesting that a purely image-based assay could potentially replace more expensive molecular assays for making disease prognostic predictions. [ABSTRACT FROM AUTHOR]

Background: The systematic analysis of imaged pathology specimens often results in a vast amount of morphological information at both the cellular and sub-cellular scales. While microscopy scanners and computerized analysis are capable of capturing and analyzing data rapidly, microscopy image data remain underutilized in research and clinical settings. One major obstacle which tends to reduce wider adoption of these new technologies throughout the clinical and scientific communities is the challenge of managing, querying, and integrating the vast amounts of data resulting from the analysis of large digital pathology datasets. This paper presents a data model, which addresses these challenges, and demonstrates its implementation in a relational database system. Context: This paper describes a data model, referred to as Pathology Analytic Imaging Standards (PAIS), and a database implementation, which are designed to support the data management and query requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines on whole-slide images and tissue microarrays (TMAs). Aims: (1) Development of a data model capable of efficiently representing and storing virtual slide related image, annotation, markup, and feature information. (2) Development of a database, based on the data model, capable of supporting queries for data retrieval based on analysis and image metadata, queries for comparison of results from different analyses, and spatial queries on segmented regions, features, and classified objects. Settings and Design: The work described in this paper is motivated by the challenges associated with characterization of micro-scale features for comparative and correlative analyses involving whole-slides tissue images and TMAs. Technologies for digitizing tissues have advanced significantly in the past decade. Slide scanners are capable of producing high-magnification, high-resolution images from whole slides and TMAs within several minutes. Hence, it is becoming increasingly feasible for basic, clinical, and translational research studies to produce thousands of whole-slide images. Systematic analysis of these large datasets requires efficient data management support for representing and indexing results from hundreds of interrelated analyses generating very large volumes of quantifications such as shape and texture and of classifications of the quantified features. Materials and Methods: We have designeda data model and a database to address the data management requirements of detailed characterization of micro-anatomic morphology through many interrelated analysis pipelines. The data model represents virtual slide related image, annotation, markup and feature information. The database supports a wide range of metadata and spatial queries on images, annotations, markups, and features. Results: We currently have three databases running on a Dell PowerEdge T410 server with CentOS 5.5 Linux operating system. The database server is IBM DB2 Enterprise Edition 9.7.2. The set of databases consists of 1) a TMA database containing image analysis results from 4740 cases of breast cancer, with 641 MB storage size; 2) an algorithm validation database, which stores markups and annotations from two segmentation algorithms and two parameter sets on 18 selected slides, with 66 GB storage size; and 3) an in silico brain tumor study database comprising results from 307 TCGA slides, with 365 GB storage size. The latter two databases also contain human-generated annotations and markups for regions and nuclei. Conclusions: Modeling and managing pathology image analysis results in a database provide immediate benefits on the value and usability of data in a research study. The database provides powerful query capabilities, which are otherwise difficult or cumbersome to support by other approaches such as programming languages. Standardized, semantic annotated data representation and interfaces also make it possible to more efficiently share image data and analysis results. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMRs) provide universal access to health care information across multidisciplinary lines. In pathology departments, transfusion and apheresis medicine services (TAMS) involved in direct patient care activities produce data and documentation that typically do not enter the EMR. Taking advantage of our institution's initiative for implementation of a paperless medical record, our TAMS division set out to develop an electronic charting (e-charting) strategy within the EMR. Methods: A focus group of our hospital's transfusion committee consisting of transfusion medicine specialists, pathologists, residents, nurses, hemapheresis specialists, and information technologists was constituted and charged with the project. The group met periodically to implement e-charting TAMS workflow and produced electronic documents within the EMR (Cerner Millenium) for various service line functions. Results: The interdisciplinary working group developed and implemented electronic versions of various paper-based clinical documentation used by these services. All electronic notes collectively gather and reside within a unique Transfusion Medicine Folder tab in the EMR, available to staff with access to patient charts. E-charting eliminated illegible handwritten notes, resulted in more consistent clinical documentation among staff, and provided greater real-time review/access of hemotherapy practices. No major impediments to workflow or inefficiencies have been encountered. However, minor updates and corrections to documents as well as select work re-designs were required for optimal use of e-charting by these services. Conclusion: Documentation of pathology subspecialty activities such as TAMS can be successfully incorporated into the EMR. E-charting by staff enhances communication and helps promote standardized documentation of patient care within and across service lines. Well-constructed electronic documents in the EMR may also enhance data mining, quality improvement, and biovigilance monitoring activities. [ABSTRACT FROM AUTHOR]

When using artificial intelligence systems, edge computing can allow end users to access data output in real-time without waiting for lengthy data-intensive analyses to be carried out externally.[[5]] Using edge computing also offers an additional opportunity for raw, sensitive data to be processed locally and rendered secure before sending it to the cloud. Examples of open-source systems include Apache Edgent,[[6]] OpenStack,[[7]] and EdgeX Foundry.[[8]] Edge computing business systems include Azure IoT Edge[[9]] and Amazon AWS Greengrass.[[10]] There are four essential technologies that enable edge computing.[[11]],[[12]] The first are virtual machines (VMs) and containers. Novelty of the LiveMicro telepathology System In their paper, Sacco et al. developed a new edge computing-based telepathology system called LiveMicro.[[1]] Their application was made accessible via a web browser. [Extracted from the article]

Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand. [ABSTRACT FROM AUTHOR]

Background: The frozen section pathology practice at Mayo Clinic in Rochester performs ~20,000 intraoperative consultations a year (~70-80/weekday). To prepare for intraoperative consultations, surgical pathology fellows and residents review the case history, previous pathology, and relevant imaging the day before surgery. Before the work described herein, review of pending surgical pathology cases was a paper-based process requiring handwritten transcription from the electronic health record, a laborious and potentially error prone process. Methods: To facilitate more efficient case review, a modular extension of an existing surgical listing software application (Surgical and Procedure Scheduling [SPS]) was developed. The module (SPS-pathology-specific module [PM]) added pathology-specific functionality including recording case notes, prefetching of radiology, pathology, and operative reports from the medical record, flagging infectious cases, and real-time tracking of cases in the operating room. After implementation, users were surveyed about its impact on the surgical pathology practice. Results: There were 16 survey respondents (five staff pathologists and eleven residents or fellows). All trainees (11/11) responded that the application improved an aspect of surgical list review including abstraction from medical records (10/11), identification of possibly infectious cases (7/11), and speed of list preparation (10/11). The average reported time savings in list preparation was 1.4 h/day. Respondents indicated the application improved the speed (11/16), clarity (13/16), and accuracy (10/16) of morning report. During the workday, respondents reported the application improved real-time case review (14/16) and situational awareness of ongoing cases (13/16). Conclusions: A majority of respondents found the SPS-PM improved all preparatory and logistical aspects of the Mayo Clinic frozen section surgical pathology practice. In addition, use of the SPS-PM saved an average of 1.4 h/day for residents and fellows engaged in preparatory case review. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest. [ABSTRACT FROM AUTHOR]

Background: Virtual microscopy and automated processing of cytological slides are more challenging compared to histological slides. Since cytological slides exhibit a three-dimensional surface and the required microscope objectives with high resolution have a low depth of field, these cannot capture all objects of a single field of view in focus. One solution would be to scan multiple focal planes; however, the increase in processing time and storage requirements are often prohibitive for clinical routine. Materials and Methods: In this paper, we show that it is a reasonable trade-off to scan a single focal plane and automatically reject defocused objects from the analysis. To this end, we have developed machine learning solutions for the automated identification of defocused objects. Our approach includes creating novel features, systematically optimizing their parameters, selecting adequate classifier algorithms, and identifying the correct decision boundary between focused and defocused objects. We validated our approach for computer-assisted DNA image cytometry. Results and Conclusions: We reach an overall sensitivity of 96.08% and a specificity of 99.63% for identifying defocused objects. Applied on ninety cytological slides, the developed classifiers automatically removed 2.50% of the objects acquired during scanning, which otherwise would have interfered the examination. Even if not all objects are acquired in focus, computer-assisted DNA image cytometry still identified more diagnostically or prognostically relevant objects compared to manual DNA image cytometry. At the same time, the workload for the expert is reduced dramatically. [ABSTRACT FROM AUTHOR]

This paper describes work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Prostatic intraepithelial neoplasia (PIN) represents premalignant tissue involving epithelial growth confined in the lumen of prostatic acini. In the attempts to understand oncogenesis in the human prostate, early neoplastic changes can be modeled in the mouse with genetic manipulation of certain tumor suppressor genes or oncogenes. As with many early pathological changes, the PIN lesions in the mouse prostate are macroscopically small, but microscopically spanning areas often larger than single high magnification focus fields in microscopy. This poses a challenge to utilize full potential of the data acquired in histological specimens. We use whole prostates fixed in molecular fixative PAXgene™, embedded in paraffin, sectioned through and stained with H&E. To visualize and analyze the microscopic information spanning whole mouse PIN (mPIN) lesions, we utilize automated whole slide scanning and stacked sections through the tissue. The region of interests is masked, and the masked areas are processed using a cascade of automated image analysis steps. The images are normalized in color space, after which exclusion of secretion areas and feature extraction is performed. Machine learning is utilized to build a model of early PIN lesions for determining the probability for histological changes based on the calculated features. We performed a feature-based analysis to mPIN lesions. First, a quantitative representation of over 100 features was built, including several features representing pathological changes in PIN, especially describing the spatial growth pattern of lesions in the prostate tissue. Furthermore, we built a classification model, which is able to align PIN lesions corresponding to grading by visual inspection to more advanced and mild lesions. The classifier allowed both determining the probability of early histological changes for uncategorized tissue samples and interpretation of the model parameters. Here, we develop quantitative image analysis pipeline to describe morphological changes in histological images. Even subtle changes in mPIN lesion characteristics can be described with feature analysis and machine learning. Constructing and using multidimensional feature data to represent histological changes enables richer analysis and interpretation of early pathological lesions. [ABSTRACT FROM AUTHOR]

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst minimizing the number of observations required. RandomSpot is a web-based tool for SRS in stereology, which systematically places equidistant points within a given region of interest on a virtual slide. Each point can then be visually inspected by a pathologist in order to generate an unbiased sample of the distribution of classes within the tissue. Further measurements can then be derived from the distribution, such as the ratio of tumor to stroma. RandomSpot replicates the fundamental principle of traditional light microscope grid-shaped graticules, with the added benefits associated with virtual slides, such as facilitated collaboration and automated navigation between points. Once the sample points have been added to the region(s) of interest, users can download the annotations and view them locally using their virtual slide viewing software. Since its introduction, RandomSpot has been used extensively for international collaborative projects, clinical trials and independent research projects. So far, the system has been used to generate over 21,000 sample sets, and has been used to generate data for use in multiple publications, identifying significant new prognostic markers in colorectal, upper gastro-intestinal and breast cancer. Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications. [ABSTRACT FROM AUTHOR]

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Quick and seamless integration between input devices and the navigation of digital slides remains a key barrier for many pathologists to "go digital." To better understand this integration, three different input device implementations were compared in terms of time to diagnose, perceived workload and users' preferences. Six pathologists reviewed in total nine cases with a computer mouse, a 6 degrees-of-freedom (6DOF) navigator and a touchpad. The participants perceived significantly less workload (P < 0.05) with the computer mouse and the 6DOF navigator, than with the touchpad, while no effect of the input device used on the time to diagnose was observed. Five out of six pathologists preferred the 6DOF navigator, while the touchpad was the least preferred device. While digital slide navigation is often designed to mimic microscope interaction, the results of this study demonstrate that in order to minimize workload there is reason to let the digital interaction move beyond the familiar microscope tradition. [ABSTRACT FROM AUTHOR]

Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images. [ABSTRACT FROM AUTHOR]

Introduction: Recent technology developments have demonstrated the benefit of using whole slide imaging (WSI) in computer-aided diagnosis. In this paper, we explore the feasibility of using automatic WSI analysis to assist grading of clear cell renal cell carcinoma (RCC), which is a manual task traditionally performed by pathologists. Materials and Methods: Automatic WSI analysis was applied to 39 hematoxylin and eosin-stained digitized slides of clear cell RCC with varying grades. Kernel regression was used to estimate the spatial distribution of nuclear size across the entire slides. The analysis results were correlated with Fuhrman nuclear grades determined by pathologists. Results: The spatial distribution of nuclear size provided a panoramic view of the tissue sections. The distribution images facilitated locating regions of interest, such as high-grade regions and areas with necrosis. The statistical analysis showed that the maximum nuclear size was significantly different (P < 0.001) between low-grade (Grades I and II) and high-grade tumors (Grades III and IV). The receiver operating characteristics analysis showed that the maximum nuclear size distinguished high-grade and low-grade tumors with a false positive rate of 0.2 and a true positive rate of 1.0. The area under the curve is 0.97. Conclusion: The automatic WSI analysis allows pathologists to see the spatial distribution of nuclei size inside the tumors. The maximum nuclear size can also be used to differentiate low-grade and high-grade clear cell RCC with good sensitivity and specificity. These data suggest that automatic WSI analysis may facilitate pathologic grading of renal tumors and reduce variability encountered with manual grading. [ABSTRACT FROM AUTHOR]

Recent technological advances have improved the whole slide imaging (WSI) scanner quality and reduced the cost of storage, thereby enabling the deployment of digital pathology for routine diagnostics. In this paper we present the experiences from two Swedish sites having deployed routine large-scale WSI for primary review. At Kalmar County Hospital, the digitization process started in 2006 to reduce the time spent at the microscope in order to improve the ergonomics. Since 2008, more than 500,000 glass slides have been scanned in the routine operations of Kalmar and the neighboring Linköping University Hospital. All glass slides are digitally scanned yet they are also physically delivered to the consulting pathologist who can choose to review the slides on screen, in the microscope, or both. The digital operations include regular remote case reporting by a few hospital pathologists, as well as around 150 cases per week where primary review is outsourced to a private clinic. To investigate how the pathologists choose to use the digital slides, a web-based questionnaire was designed and sent out to the pathologists in Kalmar and Linköping. The responses showed that almost all pathologists think that ergonomics have improved and that image quality was sufficient for most histopathologic diagnostic work. 38 ± 28% of the cases were diagnosed digitally, but the survey also revealed that the pathologists commonly switch back and forth between digital and conventional microscopy within the same case. The fact that two full-scale digital systems have been implemented and that a large portion of the primary reporting is voluntarily performed digitally shows that large-scale digitization is possible today. [ABSTRACT FROM AUTHOR]

Introduction: Digital pathology is a relatively new field. Inventors of technology in this field typically file for patents to protect their intellectual property. An understanding of the patent landscape is crucial for companies wishing to secure patent protection and market dominance for their products. To our knowledge, there has been no prior systematic review of patents related to digital pathology. Therefore, the aim of this study was to systematically identify and evaluate United States patents and patent applications related to digital pathology. Materials and Methods: Issued patents and patent applications related to digital pathology published in the United States Patent and Trademark Office (USPTO) database (www.uspto.gov) (through January 2014) were searched using the Google Patents search engine (Google Inc., Mountain View, California, USA). Keywords and phrases related to digital pathology, whole-slide imaging (WSI), image analysis, and telepathology were used to query the USPTO database. Data were downloaded and analyzed using the Papers application (Mekentosj BV, Aalsmeer, Netherlands). Results: A total of 588 United States patents that pertain to digital pathology were identified. In addition, 228 patent applications were identified, including 155 that were pending, 65 abandoned, and eight rejected. Of the 588 patents granted, 348 (59.18%) were specific to pathology, while 240 (40.82%) included more general patents also usable outside of pathology. There were 70 (21.12%) patents specific to pathology and 57 (23.75%) more general patents that had expired. Over 120 unique entities (individual inventors, academic institutions, and private companies) applied for pathology specific patents. Patents dealt largely with telepathology and image analysis. WSI related patents addressed image acquisition (scanning and focus), quality (z-stacks), management (storage, retrieval, and transmission of WSI files), and viewing (graphical user interface (GUI), workflow, slide navigation and remote control). An increasing number of recent patents focused on computer-aided diagnosis (CAD) and digital consultation networks. Conclusion: In the last 2 decades, there have been an increasing number of patents granted and patent applications filed related to digital pathology. The number of these patents quadrupled during the last decade, and this trend is predicted to intensify based on the number of patent applications already published by the USPTO. [ABSTRACT FROM AUTHOR]

Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. Results: In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. Conclusion: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned. [ABSTRACT FROM AUTHOR]

Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research. [ABSTRACT FROM AUTHOR]

Introduction: The notion of local scale was introduced to characterize varying levels of image detail so that localized image processing tasks could be performed while simultaneously yielding a globally optimal result. In this paper, we have presented the methodological framework for a novel locally adaptive scale definition, morphologic scale (MS), which is different from extant local scale definitions in that it attempts to characterize local heterogeneity as opposed to local homogeneity. Methods: At every point of interest, the MS is determined as a series of radial paths extending outward in the direction of least resistance, navigating around obstructions. Each pixel can then be directly compared to other points of interest via a rotationally invariant quantitative feature descriptor, determined by the application of Fourier descriptors to the collection of these paths. Results: Our goal is to distinguish tumor and stromal tissue classes in the context of four different digitized pathology datasets: prostate tissue microarrays (TMAs) stained with hematoxylin and eosin (HE) (44 images) and TMAs stained with only hematoxylin (H) (44 images), slide mounts of ovarian H (60 images), and HE breast cancer (51 images) histology images. Classification performance over 50 cross validation runs using a Bayesian classifier produced mean areas under the curve of 0.88 ±0.01 (prostate HE), 0.87 ±0.02 (prostate H), 0.88 ±0.01 (ovarian H), and 0.80 ±0.01 (breast HE). Conclusion: For each dataset listed in Table 3, we randomly selected 100 points per image, and using the procedure described in Experiment 1, we attempted to separate them as belonging to stroma or epithelium. [ABSTRACT FROM AUTHOR]

Context: Three dimensional (3D) tissue reconstructions from the histology images with different stains allows the spatial alignment of structural and functional elements highlighted by different stains for quantitative study of many physiological and pathological phenomena. This has significant potential to improve the understanding of the growth patterns and the spatial arrangement of diseased cells, and enhance the study of biomechanical behavior of the tissue structures towards better treatments (e.g. tissue-engineering applications). Methods: This paper evaluates three strategies for 3D reconstruction from sets of two dimensional (2D) histological sections with different stains, by combining methods of 2D multi-stain registration and 3D volumetric reconstruction from same stain sections. Setting and Design: The different strategies have been evaluated on two liver specimens (80 sections in total) stained with Hematoxylin and Eosin (H and E), Sirius Red, and Cytokeratin (CK) 7. Results and Conclusion: A strategy of using multi-stain registration to align images of a second stain to a volume reconstructed by same-stain registration results in the lowest overall error, although an interlaced image registration approach may be more robust to poor section quality. [ABSTRACT FROM AUTHOR]

Background: Segmentation of areas containing tumor cells in standard H&E histopathology images of breast (and several other tissues) is a key task for computer-assisted assessment and grading of histopathology slides. Good segmentation of tumor regions is also vital for automated scoring of immunohistochemical stained slides to restrict the scoring or analysis to areas containing tumor cells only and avoid potentially misleading results from analysis of stromal regions. Furthermore, detection of mitotic cells is critical for calculating key measures such as mitotic index; a key criteria for grading several types of cancers including breast cancer. We show that tumor segmentation can allow detection and quantification of mitotic cells from the standard H&E slides with a high degree of accuracy without need for special stains, in turn making the whole process more cost-effective. Method: Based on the tissue morphology, breast histology image contents can be divided into four regions: Tumor, Hypocellular Stroma (HypoCS), Hypercellular Stroma (HyperCS), and tissue fat (Background). Background is removed during the preprocessing stage on the basis of color thresholding, while HypoCS and HyperCS regions are segmented by calculating features using magnitude and phase spectra in the frequency domain, respectively, and performing unsupervised segmentation on these features. Results: All images in the database were hand segmented by two expert pathologists. The algorithms considered here are evaluated on three pixel-wise accuracy measures: precision, recall, and F1-Score. The segmentation results obtained by combining HypoCS and HyperCS yield high F1-Score of 0.86 and 0.89 with respect to the ground truth. Conclusions: In this paper, we show that segmentation of breast histopathology image into hypocellular stroma and hypercellular stroma can be achieved using magnitude and phase spectra in the frequency domain. The segmentation leads to demarcation of tumor margins leading to improved accuracy of mitotic cell detection. [ABSTRACT FROM AUTHOR]

Information on several papers discussed at the 2013 Molecular Med Tri―Con international conference organized by the Cambridge Healthtech Institute (CHI) held in San Francisco, California in February. Topics include molecular diagnostics, digital pathology and cancer molecular markers. Several authors presented these papers in the conference including Richard Levinson, Keith Kaplan and Sushmitha Muhkerjee.