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Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study.

Balazs Acs1, Vasiliki Pelekanou1,2, Yalai Bai1

  • 1Department of Pathology, Yale School of Medicine, New Haven, CT, USA.

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|September 6, 2018
PubMed
Summary
This summary is machine-generated.

Automated digital image analysis platforms demonstrate excellent reproducibility for Ki67 expression in breast cancer, overcoming standardization issues. These tools show consistent prognostic potential, paving the way for multi-institutional clinical utility studies.

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Area of Science:

  • Oncology
  • Pathology
  • Biomedical Engineering

Background:

  • Ki67 expression is a key prognostic marker in breast cancer.
  • Lack of standardization in Ki67 measurement hinders its widespread clinical adoption.
  • Digital image analysis (DIA) offers a potential solution for automated and standardized Ki67 quantification.

Purpose of the Study:

  • To investigate the reproducibility of Ki67 measurements across three DIA platforms (HALO, QuantCenter, QuPath).
  • To assess the inter-operator and between-platform consistency of Ki67 quantification.
  • To compare the prognostic accuracy of DIA-based Ki67 measurements with patient outcomes.

Main Methods:

  • Utilized two breast cancer patient cohorts for standardization and outcome analysis.
  • Employed Mib-1 antibody for Ki67 detection on tissue microarrays.
  • Evaluated Ki67 expression using HALO, QuantCenter, and QuPath DIA platforms with supervised classifiers.
  • Measured reproducibility using intraclass correlation coefficient (ICC).

Main Results:

  • Excellent between-DIA platform reproducibility was observed (ICC: 0.933).
  • All platforms showed excellent intra-platform reproducibility (QuPath ICC: 0.992).
  • Inter-operator reproducibility was exceptionally high (ICC: 0.962-0.995).
  • DIA platforms demonstrated similar prognostic accuracy for breast cancer patient outcomes.

Conclusions:

  • Digital image analysis platforms provide outstanding reproducibility for Ki67 assessment in breast cancer.
  • The findings support the use of DIA for standardized Ki67 quantification, including the open-source QuPath platform.
  • These results justify multi-institutional DIA studies to evaluate clinical utility in breast cancer management.