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Medical Image Analysis Oct 2016With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has... (Review)
Review
With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.
Topics: Humans; Image Processing, Computer-Assisted; Machine Learning; Pathology; Precision Medicine; Radiology; Telepathology
PubMed: 27423409
DOI: 10.1016/j.media.2016.06.037 -
Archives of Pathology & Laboratory... Apr 2018
Topics: Pathology; Professionalism
PubMed: 29565211
DOI: 10.5858/arpa.2017-0504-LE -
The American Journal of Pathology Jul 2019The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline... (Review)
Review
The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.
Topics: Humans; Pathology, Clinical; Pathology, Molecular
PubMed: 31014954
DOI: 10.1016/j.ajpath.2019.03.009 -
Cancer Cytopathology Jun 2017
Topics: Cytodiagnosis; Humans; Pathology
PubMed: 28609005
DOI: 10.1002/cncy.21825 -
The American Journal of Pathology Oct 2021Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation... (Review)
Review
Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on deep learning, promises systems that can identify pathologies in slide images with a high degree of accuracy. Generative modeling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology, including the removal of color and intensity artifacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses future directions for generative models within histopathology.
Topics: Deep Learning; Humans; Image Processing, Computer-Assisted; Models, Theoretical; Pathology; Workflow
PubMed: 33838127
DOI: 10.1016/j.ajpath.2021.02.024 -
Toxicologic Pathology Jan 2017The 35th Annual Society of Toxicologic Pathology Symposium, held in June 2016 in San Diego, California, focused on "The Basis and Relevance of Variation in Toxicologic...
The 35th Annual Society of Toxicologic Pathology Symposium, held in June 2016 in San Diego, California, focused on "The Basis and Relevance of Variation in Toxicologic Responses." In order to review the basic tenants of toxicology, a "broad brush" interactive talk that gave an overview of the Cornerstones of Toxicology was presented. The presentation focused on the historical milestones and perspectives of toxicology and through many scientific graphs, data, and real-life examples covered the three basic principles of toxicology that can be summarized, as dose matters (as does timing), people differ, and things change (related to metabolism and biotransformation).
Topics: Animals; Dose-Response Relationship, Drug; History, 19th Century; History, 20th Century; History, 21st Century; History, Ancient; Humans; Pathology; Poisoning; Toxicology
PubMed: 28068892
DOI: 10.1177/0192623316675768 -
Journal of Molecular and Cellular... Oct 2020
Topics: History, 20th Century; History, 21st Century; Humans; Mentors; Pathology
PubMed: 32422319
DOI: 10.1016/j.yjmcc.2020.05.005 -
Analytical Cellular Pathology... 2011Advances in optical designs are enabling the development of miniature microscopes that can examine tissue in situ for early anatomic and molecular indicators of disease,... (Review)
Review
Advances in optical designs are enabling the development of miniature microscopes that can examine tissue in situ for early anatomic and molecular indicators of disease, in real time, and at cellular resolution. These new devices will lead to major changes in how diseases are detected and managed, driving a shift from today's diagnostic paradigm of biopsy followed by histopathology and recommended therapy, to non-invasive point-of-care diagnosis with possible same-session definitive treatment. This shift may have major implications for the training requirements of future physicians to enable them to interpret real-time in vivo microscopic data, and will also shape the emerging fields of telepathology and telemedicine. Implementation of new technologies into clinical practice is a complex process that requires bridging gaps between clinicians, engineers and scientists. This article provides a forward-looking discussion of these issues, with a focus on malignant and pre-malignant lesions, by first highlighting some of the clinical areas where point-of-care in vivo microscopy could address unmet needs, and then by reviewing the technological challenges that are being addressed, or need to be addressed, for in vivo microscopy to become a standard clinical tool.
Topics: Animals; Diffusion of Innovation; Endoscopes; Equipment Design; Humans; Microscopy; Miniaturization; Pathology; Point-of-Care Systems; Predictive Value of Tests; Prognosis; Telepathology
PubMed: 21673433
DOI: 10.3233/ACP-2011-011 -
Histopathology Jan 2017Colour is central to the practice of pathology because of the use of coloured histochemical and immunohistochemical stains to visualize tissue features. Our reliance... (Review)
Review
Colour is central to the practice of pathology because of the use of coloured histochemical and immunohistochemical stains to visualize tissue features. Our reliance upon histochemical stains and light microscopy has evolved alongside a wide variation in slide colour, with little investigation into the implications of colour variation. However, the introduction of the digital microscope and whole-slide imaging has highlighted the need for further understanding and control of colour. This is because the digitization process itself introduces further colour variation which may affect diagnosis, and image analysis algorithms often use colour or intensity measures to detect or measure tissue features. The US Food and Drug Administration have released recent guidance stating the need to develop a method of controlling colour reproduction throughout the digitization process in whole-slide imaging for primary diagnostic use. This comprehensive review introduces applied basic colour physics and colour interpretation by the human visual system, before discussing the importance of colour in pathology. The process of colour calibration and its application to pathology are also included, as well as a summary of the current guidelines and recommendations regarding colour in digital pathology.
Topics: Color; Coloring Agents; Humans; Image Interpretation, Computer-Assisted; Microscopy; Pathology; Staining and Labeling
PubMed: 27607349
DOI: 10.1111/his.13079 -
Genes Apr 2021Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular... (Review)
Review
Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.
Topics: Animals; Biomarkers, Tumor; Humans; Image Processing, Computer-Assisted; Neoplasms; Pathology; Precision Medicine; Tumor Microenvironment
PubMed: 33917241
DOI: 10.3390/genes12040538