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Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and

Christof A Bertram1,2, Marc Aubreville3, Taryn A Donovan4

  • 1University of Veterinary Medicine, Vienna, Austria.

Veterinary Pathology
|December 30, 2021
PubMed
Summary

Computer assistance significantly improves the accuracy and consistency of mitotic count (MC) analysis in canine tumors. This AI-powered tool helps pathologists better identify mitotic figures (MFs), leading to more reliable prognostication.

Keywords:
artificial intelligenceautomated image analysiscanine cutaneous mast cell tumorscomputer assistancedeep learningdigital pathologymitotic countmitotic figures

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Area of Science:

  • Veterinary Pathology
  • Computational Pathology
  • Artificial Intelligence in Histology

Background:

  • Mitotic count (MC) is crucial for cancer prognostication but suffers from observer variability.
  • Challenges include selecting the region of interest (MC-ROI) and identifying mitotic figures (MFs).
  • Artificial intelligence (AI) offers potential for standardizing MC analysis.

Purpose of the Study:

  • To compare computer-assisted MC analysis with routine pathologist analysis for canine cutaneous mast cell tumors (ccMCTs).
  • To evaluate the impact of AI on mitotic hotspot detection, MF identification, and classification accuracy.
  • To assess improvements in interobserver consistency and pathologist performance.

Main Methods:

  • 23 pathologists analyzed whole-slide images of 50 ccMCTs using unaided and computer-assisted methods (partial and full).
  • Computer assistance involved AI-driven preselection of MC-ROI, MF candidate visualization, and confidence values.
  • Performance was compared against a ground truth established using phosphohistone H3 immunohistochemistry.

Main Results:

  • Computer assistance significantly increased interobserver consistency (ICC from 0.70 to 0.92).
  • AI-preselected MC-ROIs yielded higher MCs than manual selections.
  • Pathologist performance in MF detection improved (F1-score from 0.68 to 0.79), with a 38% reduction in false negatives.

Conclusions:

  • Computer-assisted analysis enhances the reproducibility and accuracy of MC in ccMCTs.
  • AI tools can effectively assist pathologists in identifying mitotic figures and improving prognostic stratification.
  • This technology holds promise for standardizing histological assessments in veterinary oncology.