ArticleJournal of pathology informatics2022
Integrating and validating automated digital imaging analysis of estrogen receptor immunohistochemistry in a fully digital workflow for clinical use.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- AI-Assisted Scoring Improves Interobserver Agreement in Breast Cancer Biomarker Evaluation.Cancers · 2026Article
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- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Multiparametric cellular and spatial organization in cancer tissue lesions with a streamlined pipeline.Nature biomedical engineering · 2026Article
- Estrogen Receptor-Low Positive (ER-Low) Breast Cancer: A Unique Clinical and Pathological Entity.Current oncology (Toronto, Ont.) · 2026Review
- FPNuNet: a frequency-aware prompt-guided network for nuclear segmentation and classification in immunohistochemistry images.GigaScience · 2026Article
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
- Article
- Ki67 in cytological specimens of pancreatic neuroendocrine tumors: A literature review and validation of automated quantification.Journal of pathology informatics · 2025Article
- Use of AI Histopathology in Breast Cancer Diagnosis.Medicina (Kaunas, Lithuania) · 2025Review
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine.European journal of breast health · 2025Article
- Artificial intelligence in breast cancer diagnosis: A systematic literature review.Cambridge prisms. Precision medicine · 2025Review
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis.Diagnostics (Basel, Switzerland) · 2024Article
- Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry.Journal of personalized medicine · 2024Review
- Development and validation of a clinical breast cancer tool for accurate prediction of recurrence.NPJ breast cancer · 2024Article
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
- Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.Breast cancer research : BCR · 2024Article
- Artificial intelligence's impact on breast cancer pathology: a literature review.Diagnostic pathology · 2024Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The Visiopharm automated estrogen receptor (ER) digital imaging analysis (DIA) algorithm assesses digitized ER immunohistochemistry (IHC) by segmenting tumor nuclei and detecting stained nuclei automatically. We aimed to integrate and validate this algorithm in a digital pathology workflow for clinical use. Design: The study cohort consisted of a serial collection of 97 invasive breast carcinoma specimens including 73 biopsies and 24 resections. ER IHC slides were scanned into Philips Image Management System (IMS) during our routine digital workflow and digital images were directly streamed into Visiopharm platform and analyzed using automated ER algorithm to obtain the positively stained tumor nuclei and staining intensity. ER DIA scores were compared with pathologists' manual scores. Results: The overall concordance between pathologists' reads and DIA reads was excellent (91/97, 93.8%). Pearson Correlation Coefficient of the percentage of ER positive nuclei between the original reads and VIS reads was 0.72. Six cases (3 ER-negative and 3 ER-positive) had discordant results. All 3 false negative cases had very weak ER staining and no more than 10% positivity. The causes for false positive DIA were mainly pre-analytic/pre-imaging and included intermixed benign glands in tumor area, ductal carcinoma in-situ (DCIS) components, and tissue folding. Conclusions: Automated ER DIA demonstrates excellent concordance with pathologists' scores and accurately discriminates ER positive from negative cases. Furthermore, integrating automated biomarker DIA into a busy clinical digital workflow is feasible and may save time and labor for pathologists.
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