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Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
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Deep learning assisted mitotic counting for breast cancer.

Maschenka C A Balkenhol1, David Tellez2, Willem Vreuls3

  • 1Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands. maschenka.balkenhol@radboudumc.nl.

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Automated mitotic counting in breast cancer histology using deep learning improves reproducibility. A convolutional neural network (CNN) identified hotspots and counted mitoses, enhancing accuracy compared to manual methods.

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Area of Science:

  • Pathology
  • Computational Biology
  • Oncology

Background:

  • Histological grading of invasive breast cancer relies on mitotic count, a subjective measure prone to interobserver variability.
  • Current methods involve manual counting in visually selected high-proliferative regions, introducing subjectivity.
  • Automated approaches are needed to improve the objectivity and reproducibility of mitotic counting.

Purpose of the Study:

  • To compare manual mitotic counting with deep learning-based automated counting and hotspot selection in invasive breast cancer.
  • To evaluate the impact of automated hotspot identification on the reproducibility of mitotic counts.
  • To assess the feasibility of fully automated mitotic score assessment.

Main Methods:

  • Two cohorts were used: Cohort A (n=90, prospective) and Cohort B (n=298, retrospective TNBC).
  • A convolutional neural network (CNN) was trained to detect mitotic figures and generate automated hotspots in whole slide images (WSI).
  • Manual mitotic counts on glass slides and WSI were compared with automated counts from the CNN-generated hotspots.

Main Results:

  • Interobserver agreement for manual mitotic counting on glass slides was good (kappa=0.689).
  • Using CNN-generated hotspots in WSI significantly increased agreement (kappa=0.814).
  • Automated counting by CNN in predefined hotspots showed good agreement with manual counts (average kappa=0.724).

Conclusions:

  • Manual mitotic counting is not significantly affected by assessment modality (glass slides vs. WSI).
  • Automated hotspot selection substantially improves the reproducibility of mitotic counting.
  • Fully automated mitotic score assessment is feasible and does not introduce additional bias or variability.