ArticleJournal of pathology informatics2023
Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow.
Article in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- Trial
- The role of artificial intelligence-based foundation models and "copilots" in cancer pathology: potential and challenges.Journal of experimental & clinical cancer research : CR · 2025Review
- Article
- Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration.Diagnostics (Basel, Switzerland) · 2025Article
- An Evaluation Into the Robustness of Grading of Pleural Mesothelioma Outside of Specialist Centres.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2025Article
- Predictive value of mitotic figure counts in tumor progression of non-invasive high-grade papillary urothelial carcinoma of the urinary bladder: A retrospective study from a single cancer center.Bladder (San Francisco, Calif.) · 2025Article
- Breast cancer survival prediction using an automated mitosis detection pipeline.The journal of pathology. Clinical research · 2024Article
- [Artificial intelligence for pathology-how, where, and why?]Pathologie (Heidelberg, Germany) · 2024Article
- Enhancing mitosis quantification and detection in meningiomas with computational digital pathology.Acta neuropathologica communications · 2024Article
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
Introduction: Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but is prone to subjectivity. This study investigated whether mitoses counting in BC using digital whole slide images (WSI) compares better to light microscopy (LM) when assisted by artificial intelligence (AI), and to which extent differences in digital MC (AI assisted or not) result in BR grade variations. Methods: Fifty BC patients with paired core biopsies and resections were randomly selected. Component scores for BR grade were extracted from pathology reports. MC was assessed using LM, WSI, and AI. Different modalities (LM-MC, WSI-MC, and AI-MC) were analyzed for correlation with scatterplots and linear regression, and for agreement in final BR with Cohen's κ. Results: MC modalities strongly correlated in both biopsies and resections: LM-MC and WSI-MC (R Conclusion: This first validation study shows that WSI-MC may compare better to LM-MC when using AI. Agreement between BR grade based on the different mitoses counting modalities was high. These results suggest that mitoses counting on WSI can well be done, and validate the presented AI algorithm for pathologist supervised use in daily practice. Further research is required to advance our knowledge of AI-MC, but it appears at least non-inferior to LM-MC.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.