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Assessment of mitotic activity in breast cancer: revisited in the digital pathology era
  1. Asmaa Ibrahim1,2,
  2. Ayat Lashen1,3,
  3. Michael Toss1,
  4. Raluca Mihai4,
  5. Emad Rakha1,3
  1. 1Division of Cancer and Stem Cell, University of Nottingham, Nottingham, UK
  2. 2Department of Pathology, Suez Canal University, Ismailia, Egypt
  3. 3Department of Pathology, Menoufia University, Shebin El-Kom, Egypt
  4. 4Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK
  1. Correspondence to Professor Emad Rakha, Division of Cancer and Stem Cell, University of Nottingham, Nottingham, City Hospital Campus, Hucknall Road, NG5 1PB, UK; emad.rakha{at}nottingham.ac.uk

Abstract

The assessment of cell proliferation is a key morphological feature for diagnosing various pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic figures in routine histological sections remains the gold-standard method to evaluate the proliferative activity and grading of cancer. Despite the apparent simplicity of such a well-established method, visual assessment of mitotic figures in breast cancer (BC) remains a challenging task with low concordance among pathologists which can lead to under or overestimation of tumour grade and hence affects management. Guideline recommendations for counting mitoses in BC have been published to standardise methodology and improve concordance; however, the results remain less satisfactory. Alternative approaches such as the use of the proliferation marker Ki67 have been recommended but these did not show better performance in terms of concordance or prognostic stratification. The advent of whole slide image technology has brought the issue of mitotic counting in BC into the light again with more challenges to develop objective criteria for identifying and scoring mitotic figures in digitalised images. Using reliable and reproducible morphological criteria can provide the highest degree of concordance among pathologists and could even benefit the further application of artificial intelligence (AI) in breast pathology, and this relies mainly on the explicit description of these figures. In this review, we highlight the morphology of mitotic figures and their mimickers, address the current caveats in counting mitoses in breast pathology and describe how to strictly apply the morphological criteria for accurate and reliable histological grade and AI models.

  • morphology
  • breast
  • diagnostic techniques and procedures

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Footnotes

  • Handling editor Cheok Soon Lee.

  • Contributors AI wrote the initial manuscript draft. AL, MT and RM contributed in writing and critically reviewed the article. ER conceived and planned the paper, contributed in writing, made critical revisions and approved final version.

  • Funding AI and AL are supported by and funded by the Egyptian Ministry of Higher Education and Scientific Research.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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