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Mod Pathol. 2019 Jan;32(1):59-69. doi: 10.1038/s41379-018-0109-4. Epub 2018 Aug 24.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

Author information

1
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. david.rimm@yale.edu.
2
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
3
Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
4
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
5
Department of Pathology and Molecular Medicine, Juravinski Hospital and Cancer Centre, McMaster University, Hamilton, ON, Canada.
6
Transformative Pathology, Ontario Institute for Cancer Research, Toronto, ON, Canada.
7
Biomarkers & Companion Diagnostics Group, Edinburgh Cancer Research Centre, Edinburgh, UK.
8
Sinai Health System, University of Toronto, Toronto, ON, Canada.
9
Translational Laboratories, Alberta Health Services, Tom Baker Cancer Centre, Calgary, AB, Canada.
10
Institut für Pathologie and German Cancer Consortium (DKTK), Charité Campus Mitte, Berlin, Germany.
11
MolecularMD, Portland, OR, USA.
12
Optra Technologies, NeoPro SEZ, Blue Ridge, Hinjewadi, India.
13
Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
14
National Center of Pathology, Vilnius University Hospital Santara Clinics, Vilnius University, Vilnius, Lithuania.
15
Department of Medical Pathology and Laboratory Medicine, University of California Davis Medical Center, Sacramento, CA, USA.
16
Cancer Diagnostic Quality Assurance Services CIC, Poundbury Cancer Institute, Poundbury, DT, UK.
17
Department of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
18
Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
19
Breast Oncology Program, Department of Internal Medicine, University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, USA.
20
Institute of Cancer Research, London, UK.

Abstract

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

PMID:
30143750
DOI:
10.1038/s41379-018-0109-4

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