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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Mitotic figure recognition: agreement among pathologists and computerized detector.

Christopher Malon1, Elena Brachtel, Eric Cosatto

  • 1Department of Machine Learning, NEC Laboratories America, NJ 08540, USA. malon@nec-labs.com

Analytical Cellular Pathology (Amsterdam)
|October 4, 2011
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Summary

Pathologist agreement on mitotic grading in breast cancer is modest. A new computerized system for mitosis detection shows competitive performance, aiding pathologists and improving reproducibility.

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Area of Science:

  • Pathology
  • Computational Pathology
  • Digital Pathology

Background:

  • Mitotic count is crucial for Bloom-Richardson grading in breast cancer.
  • Pathologist agreement on mitotic grading is often modest, impacting diagnostic consistency.

Purpose of the Study:

  • To evaluate pathologist agreement on individual mitotic figures.
  • To train and assess a computerized system for mitosis detection.
  • To compare the system's performance against individual pathologists.

Main Methods:

  • Collected over 4,200 candidate mitotic figures from digital micrographs.
  • Trained a computerized system for automated mitosis detection.
  • Compared system and pathologist classifications on a ground truth set.

Main Results:

  • Pathologist agreement on mitotic figures ranged from slight to moderate, with significant biases.
  • The computerized system demonstrated competitive performance against individual pathologists.
  • The system's classifications aligned well with the majority pathologist consensus.

Conclusions:

  • Automated mitosis detection systems can achieve competitive performance.
  • Such systems have the potential to enhance pathologist efficiency and diagnostic reproducibility.
  • This work represents a step towards reliable automated mitosis counting in breast cancer pathology.