ReviewBreast (Edinburgh, Scotland)2020
Artificial intelligence in digital breast pathology: Techniques and applications.
Review in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 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.
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Who cites it
74 citing papers in PubMed.
- Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis.Scientific reports · 2021Trial
- Lipid Metabolism-Related Rapid Pathological Diagnosis and Staging of Breast Tissue Sections Using Two-Photon Fluorescence Imaging Assisted with a Carbonized Polymer Dot Probe.Chemical & biomedical imaging · 2026Article
- Clinical value of artificial intelligence computer-aided diagnosis system in lymph node ultrasonography: A STARD-compliant article.Medicine · 2026Article
- Artificial Intelligence and Digital Pathology for Preoperative Lymphovascular Invasion and Metastatic Risk Prediction in Breast Cancer: Bridging the Biopsy-Resection Gap.Journal of clinical medicine · 2026Review
- Longitudinal evaluation of tumor-infiltrating lymphocyte scoring using automated region of interest registration in breast cancer.Breast cancer research : BCR · 2026Article
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- The Underlying Mechanisms and Emerging Strategies to Overcome Resistance in Breast Cancer.Cancers · 2025Review
- Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.NPJ precision oncology · 2025Article
- Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.Nature communications · 2025Article
- Hypoxia-driven angiogenesis in breast cancer mechanisms and therapeutic targets: a narrative review.Annals of medicine and surgery (2012) · 2025Review
- Article
- Enhanced nuclear information fusion and visual transformer for pathological breast cancer image classification.Scientific reports · 2025Article
- Oxygen deprivation in breast cancer: mechanisms, pathways, and implications.Annals of medicine and surgery (2012) · 2025Review
- An Information-Extreme Algorithm for Universal Nuclear Feature-Driven Automated Classification of Breast Cancer Cells.Diagnostics (Basel, Switzerland) · 2025Article
- Transfer learning drives automatic HER2 scoring on HE-stained WSIs for breast cancer: a multi-cohort study.Breast cancer research : BCR · 2025Article
- The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Recent patents on anti-cancer drug discovery · 2025Review
- The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.Current pharmaceutical design · 2025Review
- Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer.Biomolecules & biomedicine · 2024Article
- A universal immunohistochemistry analyzer for generalizing AI-driven assessment of immunohistochemistry across immunostains and cancer types.NPJ precision oncology · 2024Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.
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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.