scholarly journals Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images

2021 ◽  
Author(s):  
Ayat Lashen ◽  
Asmaa Ibrahim ◽  
Ayaka Katayama ◽  
Graham Ball ◽  
Raluca Mihai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Comparative Study ◽  
Light Microscopy ◽  
Visual Assessment ◽  
Mitotic Figures ◽  
Whole Slide Images

scholarly journals A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Marc Aubreville ◽  
Christof A. Bertram ◽  
Taryn A. Donovan ◽  
Christian Marzahl ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Representation Learning ◽  
Breast Cancer Dataset ◽  
Human Breast ◽  
Cancer Dataset ◽  
Slide Image ◽  
Similar Appearance ◽  
Mitotic Figures ◽  
Whole Slide Images

AbstractCanine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.

Assessment of mitotic activity in breast cancer: revisited in the digital pathology era

2021 ◽  
pp. jclinpath-2021-207742
Author(s):  
Asmaa Ibrahim ◽  
Ayat Lashen ◽  
Michael Toss ◽  
Raluca Mihai ◽  
Emad Rakha
Keyword(s):  
Breast Cancer ◽  
Histological Grade ◽  
Digital Pathology ◽  
Visual Assessment ◽  
Breast Pathology ◽  
Gold Standard Method ◽  
Morphological Criteria ◽  
Slide Image ◽  
Prognostic Stratification ◽  
Mitotic Figures

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.

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