ArticleCellular oncology (Dordrecht, Netherlands)2019
Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer.
Article in Cellular oncology (Dordrecht, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Trial
- Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.FEBS open bio · 2026Article
- Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer.NPJ breast cancer · 2024Article
- PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.Breast cancer research : BCR · 2023Article
- Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer.NPJ breast cancer · 2022Article
- Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.Breast (Edinburgh, Scotland) · 2021Article
- Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays.Virchows Archiv : an international journal of pathology · 2020Article
- Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses.Diagnostic pathology · 2020Article
- Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's.Tomography (Ann Arbor, Mich.) · 2020Article
- Article
- Deep learning assisted mitotic counting for breast cancer.Laboratory investigation; a journal of technical methods and pathology · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposeThe prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we assessed the prognostic value of absolute mitotic counts for TNBC, using both deep learning and manual procedures.
methodsA retrospective TNBC cohort (n = 298) was used. The absolute manual mitotic count was assessed by averaging counts from three independent observers. Deep learning was performed using a convolutional neural network on digitized H&E slides. Multivariable Cox regression models for relapse-free survival and overall survival served as baseline models. These were expanded with dichotomized mitotic counts, attempting every possible cut-off value, and evaluated by means of the c-statistic.
resultsWe found that per 2 mm
conclusionsBased on our results we conclude that the level of proliferation, as reflected by mitotic count, does not serve as a prognostic factor for TNBC. Therefore, TNBC patient management based on mitotic count should be discouraged.
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Registered trials
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