ReviewHistochemistry and cell biology2008
Virtual microscopy as an enabler of automated/quantitative assessment of protein expression in TMAs.
Review in Histochemistry and cell biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 46 citations in OpenAlex.
- Virtual Microscopy Goes Global: The Images Are Virtual and the Problems Are Real.Advances in experimental medicine and biology · 2023Article
- Comparative Analysis of Immunohistochemical Staining Intensity Determined by Light Microscopy, ImageJ and QuPath in Placental Hofbauer Cells.Acta histochemica et cytochemica · 2021Article
- Validation of a Muscle-Specific Tissue Image Analysis Tool for Quantitative Assessment of Dystrophin Staining in Frozen Muscle Biopsies.Archives of pathology & laboratory medicine · 2019Article
- Proposals for best-quality immunohistochemical staining of paraffin-embedded brain tissue slides in forensics.International journal of legal medicine · 2018Article
- Proceedings of the 2017 National Toxicology Program Satellite Symposium.Toxicologic pathology · 2017Article
- Construction and analysis of tissue microarrays in the era of digital pathology: a pilot study targeting CDX1 and CDX2 in a colon cancer cohort of 612 patients.The journal of pathology. Clinical research · 2017Article
- Digital imaging of colon tissue: method for evaluation of inflammation severity by spatial frequency features of the histological images.Diagnostic pathology · 2015Article
- Evaluation of cytokeratin-19 in breast cancer tissue samples: a comparison of automatic and manual evaluations of scanned tissue microarray cylinders.Biomedical engineering online · 2015Article
- Elevated LIM kinase 1 in nonmetastatic prostate cancer reflects its role in facilitating androgen receptor nuclear translocation.Molecular cancer therapeutics · 2015Article
- Quantification of myocardial fibrosis by digital image analysis and interactive stereology.Diagnostic pathology · 2014Article
- Garbage in, garbage out: a critical evaluation of strategies used for validation of immunohistochemical biomarkers.Molecular oncology · 2014Review
- Reduced LIMK2 expression in colorectal cancer reflects its role in limiting stem cell proliferation.Gut · 2014Article
- Observer performance in the use of digital and optical microscopy for the interpretation of tissue-based biomarkers.Analytical cellular pathology (Amsterdam) · 2014Article
- Cell and tissue microarray technologies for protein and nucleic acid expression profiling.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2013Article
- Reproducibility in the automated quantitative assessment of HER2/neu for breast cancer.Journal of pathology informatics · 2013Article
- Image analysis tools for evaluation of microscopic views of immunohistochemically stained specimen in medical research-a review.Journal of medical systems · 2012Review
- Article
- Image analysis as an adjunct to manual HER-2 immunohistochemical review: a diagnostic tool to standardize interpretation.Histopathology · 2010Article
- Molecular pathology in epidemiologic studies: a primer on key considerations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2010Review
- Extending the knowledge in histochemistry and cell biology.Histochemistry and cell biology · 2010Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissue Microarrays facilitate high-throughput immuohistochemistry; however, there are key bottlenecks apparent in their analysis, particularly when conducting microscope-based manual reviews. Traditionally Tissue Microarray assessments were performed using a microscope where results were either transcribed or dictated and subsequently entered into flat-file spreadsheets. This process is labour intensive, prone to error and negates the advantages of the high-throughput Tissue Microarray format. In addition, human interpretations of staining intensity parameters are highly subjective and therefore prone to inter- and intra-observer variability. The advent of Virtual Slides has permitted the review of tissue slides across the Internet. In addition, this new technology enables the creation of software solutions to assist in the manual and automated review of Tissue Microarrays, through the use of computer aided image analysis. There are numerous academically developed and commercially available applications which assist in Tissue Microarray reviews; functionality of these systems range in complexity and application domains. The review which follows describes these systems and outlines technical considerations to be assessed when deciding on a Tissue Microarray workflow solution.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.