ArticleData in brief2023
Breast cancer dataset with biomarker Biglycan.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed, 7 citations in OpenAlex.
- Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.Cancer biomarkers : section A of Disease markers · 2024Article
- The Landscape of Small Leucine-Rich Proteoglycan Impact on Cancer Pathogenesis with a Focus on Biglycan and Lumican.Cancers · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This dataset is composed of photomicrographs of the immunohistochemical expression of Biglycan (BGN) in breast tissue, with and without cancer, using only the staining of 3-3' diaminobenzidine (DAB), after processing images with color deconvolution plugin, from Image J. The immunohistochemical DAB expression of BGN was obtained using the monoclonal antibody (M01) (clone 4E1-1G7 - Abnova Corporation, mouse anti-human). Photomicrographs were obtained, under standard conditions, using an optical microscope, with UPlanFI 100x objective (resolution: 2.75 mm), yielding an image size of 4800 × 3600 pixels. After color deconvolution, the dataset with 336 images was divided into 2 two categories: (I) with cancer and (II) without cancer. This dataset allows the training and validation of machine learning models to diagnose, recognize and classify the presence of breast cancer, using the intensity of the colors of the BGN.
Indexed as
Identifiers
What Socratic 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 Socratic graph.