Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

Shazia Akbar, Lee B. Jordan, Colin A. Purdie, Alastair M. Thompson, Stephen J. McKenna (Lead / Corresponding author)

Research output: Contribution to journalArticle

22 Citations (Scopus)
265 Downloads (Pure)

Abstract

Background
Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer generated analyses of TMAs have the potential to lessen the burden of expert pathologist review.

Methods
In current commercial software computerised ER scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually-obtained segmentation masks were used to obtain IHC scores for thirty-two ER stained invasive breast cancer TMA samples using FDA-approved IHC scoring software.

Results
Whilst pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ= 0.81) than between pathologists’ masks (κ = 0.91), this had little impact on computed IHC scores (Allred; κ = 0.91, Quickscore; κ = 0.92).

Conclusion
The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials.
Original languageEnglish
Pages (from-to)1075-1080
Number of pages6
JournalBritish Journal of Cancer
Volume113
Issue number7
Early online date8 Sep 2015
DOIs
Publication statusPublished - 29 Sep 2015

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