Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer.

Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer.

Oncotarget. 2018 Feb 20;9(14):11619-11630

Authors: Tay TKY, Thike AA, Pathmanathan N, Jara-Lazaro AR, Iqbal J, Sng ASH, Ye HS, Lim JCT, Koh VCY, Tan JSY, Yeong JPS, Chow ZL, Li HH, Cheng CL, Tan PH

Abstract
Background: Ki67 positivity in invasive breast cancers has an inverse correlation with survival outcomes and serves as an immunohistochemical surrogate for molecular subtyping of breast cancer, particularly ER positive breast cancer. The optimal threshold of Ki67 in both settings, however, remains elusive. We use computer assisted image analysis (CAIA) to determine the optimal threshold for Ki67 in predicting survival outcomes and differentiating luminal B from luminal A breast cancers.
Methods: Quantitative scoring of Ki67 on tissue microarray (TMA) sections of 440 invasive breast cancers was performed using Aperio ePathology ImmunoHistochemistry Nuclear Image Analysis algorithm, with TMA slides digitally scanned via Aperio ScanScope XT System.
Results: On multivariate analysis, tumours with Ki67 ≥14% had an increased likelihood of recurrence (HR 1.941, p=0.021) and shorter overall survival (HR 2.201, p=0.016). Similar findings were observed in the subset of 343 ER positive breast cancers (HR 2.409, p=0.012 and HR 2.787, p=0.012 respectively). The value of Ki67 associated with ER+HER2-PR<20% tumours (Luminal B subtype) was found to be <17%.
Conclusion: Using CAIA, we found optimal thresholds for Ki67 that predict a poorer prognosis and an association with the Luminal B subtype of breast cancer. Further investigation and validation of these thresholds are recommended.

PMID: 29545924 [PubMed]

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