ArticleThe journal of pathology. Clinical research2024
Breast cancer survival prediction using an automated mitosis detection pipeline.
Article in The journal of pathology. Clinical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.FEBS open bio · 2026Article
- Applications of Artificial Intelligence (AI) in Breast Cancer Care Delivery and Education: A Scoping Review.International journal of environmental research and public health · 2026Article
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis.Scientific data · 2026Article
- Computer Vision-Assisted Spatial Analysis of Mitoses and Vasculature in Lung Cancer.Journal of clinical medicine · 2025Article
- Bioinformatics analysis of IFI6 as a novel prognostic biomarker and its correlation with immune infiltration in breast cancer.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm
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Registered trials
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