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Pathology, Research and Practice Sep 2020Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows... (Review)
Review
Information, archives, and intelligent artificial systems are part of everyday life in modern medicine. They already support medical staff by mapping their workflows with shared availability of cases' referral information, as needed for example, by the pathologist, and this support will be increased in the future even more. In radiology, established standards define information models, data transmission mechanisms, and workflows. Other disciplines, such as pathology, cardiology, and radiation therapy, now define further demands in addition to these established standards. Pathology may have the highest technical demands on the systems, with very complex workflows, and the digitization of slides generating enormous amounts of data up to Gigabytes per biopsy. This requires enormous amounts of data to be generated per biopsy, up to the gigabyte range. Digital pathology allows a change from classical histopathological diagnosis with microscopes and glass slides to virtual microscopy on the computer, with multiple tools using artificial intelligence and machine learning to support pathologists in their future work.
Topics: Artificial Intelligence; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Pathologists; Pathology; Workflow
PubMed: 32825928
DOI: 10.1016/j.prp.2020.153040 -
Modern Pathology : An Official Journal... Feb 2022The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry... (Review)
Review
The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.
Topics: Humans; Microscopy; Pathologists; Pathology, Clinical
PubMed: 34599281
DOI: 10.1038/s41379-021-00929-0 -
The Journal of Pathology Jul 2022Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to... (Review)
Review
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Topics: Artificial Intelligence; COVID-19; Humans; Lung; Lung Neoplasms; Pathologists
PubMed: 35579955
DOI: 10.1002/path.5966 -
JAMA Dec 2017Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. (Comparative Study)
Comparative Study
IMPORTANCE
Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency.
OBJECTIVE
Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting.
DESIGN, SETTING, AND PARTICIPANTS
Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC).
EXPOSURES
Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation.
MAIN OUTCOMES AND MEASURES
The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor.
RESULTS
The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC).
CONCLUSIONS AND RELEVANCE
In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
Topics: Algorithms; Breast Neoplasms; Female; Humans; Lymphatic Metastasis; Machine Learning; Pathologists; Pathology, Clinical; ROC Curve
PubMed: 29234806
DOI: 10.1001/jama.2017.14585 -
Archives of Pathology & Laboratory... Jul 2019Skin adnexal tumors, those neoplasms deriving from hair follicles and sweat glands, are often a source of confusion amongst even experienced pathologists. Many...
CONTEXT.—
Skin adnexal tumors, those neoplasms deriving from hair follicles and sweat glands, are often a source of confusion amongst even experienced pathologists. Many well-described entities have overlapping features, tumors are often only partially sampled, and many cases do not fit neatly into well-established classification schemes.
OBJECTIVES.—
To simplify categorization of adnexal tumors for the general surgical pathologist and to shed light on many of the diagnostic dilemmas commonly encountered in daily practice. The following review breaks adnexal neoplasms into 3 groups: sebaceous, sweat gland-derived, and follicular.
DATA SOURCES.—
Pathology reference texts and primary literature regarding adnexal tumors.
CONCLUSIONS.—
Review of the clinical and histopathologic features of primary cutaneous adnexal tumors, and the diagnostic dilemmas they create, will assist the general surgical pathologist in diagnosing these often challenging lesions.
Topics: Humans; Neoplasms, Adnexal and Skin Appendage; Pathologists; Pathology, Surgical; Skin Neoplasms
PubMed: 30638401
DOI: 10.5858/arpa.2018-0189-RA -
Pathology Feb 2017Breast cancer is a heterogeneous disease featuring distinct histological, molecular and clinical phenotypes. Although traditional classification systems utilising...
Breast cancer is a heterogeneous disease featuring distinct histological, molecular and clinical phenotypes. Although traditional classification systems utilising clinicopathological and few molecular markers are well established and validated, they remain insufficient to reflect the diverse biological and clinical heterogeneity of breast cancer. Advancements in high-throughput molecular techniques and bioinformatics have contributed to the improved understanding of breast cancer biology, refinement of molecular taxonomies and the development of novel prognostic and predictive molecular assays. Application of such technologies is already underway, and is expected to change the way we manage breast cancer. Despite the enormous amount of work that has been carried out to develop and refine breast cancer molecular prognostic and predictive assays, molecular testing is still in evolution. Pathologists should be aware of the new technology and be ready for the challenge. In this review, we provide an update on the application of molecular techniques with regard to breast cancer diagnosis, prognosis and outcome prediction. The current contribution of emerging technology to our understanding of breast cancer is also highlighted.
Topics: Biomarkers, Tumor; Breast Neoplasms; Female; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Pathologists; Prognosis
PubMed: 28040199
DOI: 10.1016/j.pathol.2016.10.012 -
Archives of Pathology & Laboratory... Jan 2023
Topics: Humans; Pathologists
PubMed: 36577091
DOI: 10.5858/arpa.2022-0226-ED -
Archives of Pathology & Laboratory... Apr 2020
Topics: Career Choice; Career Mobility; Humans; Pathologists; Pathology, Clinical
PubMed: 31971465
DOI: 10.5858/arpa.2019-0680-ED -
Archives of Pathology & Laboratory... Jun 2018
Topics: Humans; Pathologists; Pathology; Social Media
PubMed: 29848029
DOI: 10.5858/arpa.2017-0576-ED -
Archives of Pathology & Laboratory... Feb 2018
Topics: Humans; Neoplasms; Pathologists; Patient Safety; United States
PubMed: 29166144
DOI: 10.5858/arpa.2017-0443-LE