ArticleJournal of pathology informatics2017
Impact of Altering Various Image Parameters on Human Epidermal Growth Factor Receptor 2 Image Analysis Data Quality.
Article in Journal of pathology informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists.Archives of pathology & laboratory medicine · 2019Guideline
- Comparison and image analysis of different file formats on whole-slide images for colorectal and non-small cell lung carcinomas.Journal of pathology informatics · 2026Article
- Use of quality checks and processes across digital histopathology: an initial survey from the Bigpicture consortium.Journal of clinical pathology · 2025Article
- Unlocking the potential of digital pathology: Novel baselines for compression.Journal of pathology informatics · 2025Article
- Artificial intelligence applied to breast pathology.Virchows Archiv : an international journal of pathology · 2022Review
- Improved 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation in radiotherapy.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.NPJ breast cancer · 2020Review
- Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
introductionThe quality of data obtained from image analysis can be directly affected by several preanalytical (e.g., staining, image acquisition), analytical (e.g., algorithm, region of interest [ROI]), and postanalytical (e.g., computer processing) variables. Whole-slide scanners generate digital images that may vary depending on the type of scanner and device settings. Our goal was to evaluate the impact of altering brightness, contrast, compression, and blurring on image analysis data quality.
methodsSlides from 55 patients with invasive breast carcinoma were digitized to include a spectrum of human epidermal growth factor receptor 2 (HER2) scores analyzed with Visiopharm (30 cases with score 0, 10 with 1+, 5 with 2+, and 10 with 3+). For all images, an ROI was selected and four parameters (brightness, contrast, JPEG2000 compression, out-of-focus blurring) then serially adjusted. HER2 scores were obtained for each altered image.
resultsHER2 scores decreased with increased illumination, higher compression ratios, and increased blurring. HER2 scores increased with greater contrast. Cases with HER2 score 0 were least affected by image adjustments.
conclusionThis experiment shows that variations in image brightness, contrast, compression, and blurring can have major influences on image analysis results. Such changes can result in under- or over-scoring with image algorithms. Standardization of image analysis is recommended to minimize the undesirable impact such variations may have on data output.
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Registered trials
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