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Deep learning assisted mitotic counting for breast cancer
Radboud Univ Nijmegen, Netherlands.
Radboud Univ Nijmegen, Netherlands.
Canisius Wilhelmina Hosp, Netherlands.
Haaglanden Med Ctr, Netherlands.
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2019 (English)In: Laboratory Investigation, ISSN 0023-6837, E-ISSN 1530-0307, Vol. 99, no 11, p. 1596-1606Article in journal (Refereed) Published
Abstract [en]

As part of routine histological grading, for every invasive breast cancer the mitotic count is assessed by counting mitoses in the (visually selected) region with the highest proliferative activity. Because this procedure is prone to subjectivity, the present study compares visual mitotic counting with deep learning based automated mitotic counting and fully automated hotspot selection. Two cohorts were used in this study. Cohort A comprised 90 prospectively included tumors which were selected based on the mitotic frequency scores given during routine glass slide diagnostics. This pathologist additionally assessed the mitotic count in these tumors in whole slide images (WSI) within a preselected hotspot. A second observer performed the same procedures on this cohort. The preselected hotspot was generated by a convolutional neural network (CNN) trained to detect all mitotic figures in digitized hematoxylin and eosin (Hamp;E) sections. The second cohort comprised a multicenter, retrospective TNBC cohort (n = 298), of which the mitotic count was assessed by three independent observers on glass slides. The same CNN was applied on this cohort and the absolute number of mitotic figures in the hotspot was compared to the averaged mitotic count of the observers. Baseline interobserver agreement for glass slide assessment in cohort A was good (kappa 0.689; 95% CI 0.580-0.799). Using the CNN generated hotspot in WSI, the agreement score increased to 0.814 (95% CI 0.719-0.909). Automated counting by the CNN in comparison with observers counting in the predefined hotspot region yielded an average kappa of 0.724. We conclude that manual mitotic counting is not affected by assessment modality (glass slides, WSI) and that counting mitotic figures in WSI is feasible. Using a predefined hotspot area considerably improves reproducibility. Also, fully automated assessment of mitotic score appears to be feasible without introducing additional bias or variability.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 99, no 11, p. 1596-1606
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:liu:diva-162059DOI: 10.1038/s41374-019-0275-0ISI: 000493299900001PubMedID: 31222166OAI: oai:DiVA.org:liu-162059DiVA, id: diva2:1370993
Note

Funding Agencies|Radboud University Medical Center Institute for Health Sciences (RIHS)

Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2019-11-18

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van der Laak, Jeroen
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Division of Radiological SciencesFaculty of Medicine and Health SciencesClinical pathologyCenter for Medical Image Science and Visualization (CMIV)
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