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An inter-observer Ki67 reproducibility study applying two different assessment methods: on behalf of the Danish Scientific Committee of Pathology, Danish breast cancer cooperative group (DBCG)

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INTRODUCTION: In 2011, the St. Gallen Consensus Conference introduced the use of pathology to define the intrinsic breast cancer subtypes by application of immunohistochemical (IHC) surrogate markers ER, PR, HER2 and Ki67 with a specified Ki67 cutoff (>14%) for luminal B-like definition. Reports concerning impaired reproducibility of Ki67 estimation and threshold inconsistency led to the initiation of this quality assurance study (2013-2015). The aim of the study was to investigate inter-observer variation for Ki67 estimation in malignant breast tumors by two different quantification methods (assessment method and count method) including measure of agreement between methods.

MATERIAL AND METHODS: Fourteen experienced breast pathologists from 12 pathology departments evaluated 118 slides from a consecutive series of malignant breast tumors. The staining interpretation was performed according to both the Danish and Swedish guidelines. Reproducibility was quantified by intra-class correlation coefficient (ICC) and Lights Kappa with dichotomization of observations at the larger than (>) 20% threshold. The agreement between observations by the two quantification methods was evaluated by Bland-Altman plot.

RESULTS: For the fourteen raters the median ranged from 20% to 40% by the assessment method and from 22.5% to 36.5% by the count method. Light's Kappa was 0.664 for observation by the assessment method and 0.649 by the count method. The ICC was 0.82 (95% CI: 0.77-0.86) by the assessment method vs. 0.84 (95% CI: 0.80-0.87) by the count method.

CONCLUSION: Although the study in general showed a moderate to good inter-observer agreement according to both ICC and Lights Kappa, still major discrepancies were identified in especially the mid-range of observations. Consequently, for now Ki67 estimation is not implemented in the DBCG treatment algorithm.

TidsskriftActa oncologica
Udgave nummer1
Sider (fra-til)83-89
Antal sider7
StatusUdgivet - jan. 2018

ID: 52601131