External validation of the ImmunoRatio image analysis application for ERα determination in breast cancer

Sreekumar Sundara Rajan; Kieran Horgan; Valerie Speirs; Andrew M. Hanby

doi:10.1136/jclinpath-2013-201680

External validation of the ImmunoRatio image analysis application for ERα determination in breast cancer

Sreekumar Sundara Rajan, Kieran Horgan, Valerie Speirs, Andrew M. Hanby

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

The aim of this study was to validate ImmunoRatio, a web-based automated image analysis application, by comparing the manual and automated analysis scores for oestrogen receptor α (ERα) in breast carcinomas. Tissue microarrays comprising 200 breast cancer cases prestained for ERα were scanned and scored manually using ImageScope viewing software. Corresponding images were then uploaded and assessed according to the web-based ImmunoRatio programme. There was excellent correlation between manual and ImmunoRatio ERα scores (Spearman correlation=0.872; p≥0.001). The manual and ImmunoRatio ERα scores showed only a moderate agreement (κ=0.421; Weighted kappa=0.874 (CI 0.839 to 0.902)), most probably due to lack of specificity of the algorithm to differentiate between cancer and non-cancer nuclei. Further development to enable differentiation of cancer and non-cancer elements should improve the specificity of the application. Our results support the use of ImmunoRatio software for analysing ERα immunohistochemistry in breast cancer tissues for the purposes of research.

Original language	English
Pages (from-to)	72-75
Number of pages	4
Journal	Journal of Clinical Pathology
Volume	67
Issue number	1
Early online date	28 Aug 2013
DOIs	https://doi.org/10.1136/jclinpath-2013-201680
Publication status	Published - Jan 2014
Externally published	Yes

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title = "External validation of the ImmunoRatio image analysis application for ERα determination in breast cancer",

abstract = "The aim of this study was to validate ImmunoRatio, a web-based automated image analysis application, by comparing the manual and automated analysis scores for oestrogen receptor α (ERα) in breast carcinomas. Tissue microarrays comprising 200 breast cancer cases prestained for ERα were scanned and scored manually using ImageScope viewing software. Corresponding images were then uploaded and assessed according to the web-based ImmunoRatio programme. There was excellent correlation between manual and ImmunoRatio ERα scores (Spearman correlation=0.872; p≥0.001). The manual and ImmunoRatio ERα scores showed only a moderate agreement (κ=0.421; Weighted kappa=0.874 (CI 0.839 to 0.902)), most probably due to lack of specificity of the algorithm to differentiate between cancer and non-cancer nuclei. Further development to enable differentiation of cancer and non-cancer elements should improve the specificity of the application. Our results support the use of ImmunoRatio software for analysing ERα immunohistochemistry in breast cancer tissues for the purposes of research. ",

author = "{Sundara Rajan}, Sreekumar and Kieran Horgan and Valerie Speirs and Hanby, {Andrew M.}",

year = "2014",

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doi = "10.1136/jclinpath-2013-201680",

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AU - Sundara Rajan, Sreekumar

AU - Horgan, Kieran

AU - Speirs, Valerie

AU - Hanby, Andrew M.

PY - 2014/1

Y1 - 2014/1

N2 - The aim of this study was to validate ImmunoRatio, a web-based automated image analysis application, by comparing the manual and automated analysis scores for oestrogen receptor α (ERα) in breast carcinomas. Tissue microarrays comprising 200 breast cancer cases prestained for ERα were scanned and scored manually using ImageScope viewing software. Corresponding images were then uploaded and assessed according to the web-based ImmunoRatio programme. There was excellent correlation between manual and ImmunoRatio ERα scores (Spearman correlation=0.872; p≥0.001). The manual and ImmunoRatio ERα scores showed only a moderate agreement (κ=0.421; Weighted kappa=0.874 (CI 0.839 to 0.902)), most probably due to lack of specificity of the algorithm to differentiate between cancer and non-cancer nuclei. Further development to enable differentiation of cancer and non-cancer elements should improve the specificity of the application. Our results support the use of ImmunoRatio software for analysing ERα immunohistochemistry in breast cancer tissues for the purposes of research.

AB - The aim of this study was to validate ImmunoRatio, a web-based automated image analysis application, by comparing the manual and automated analysis scores for oestrogen receptor α (ERα) in breast carcinomas. Tissue microarrays comprising 200 breast cancer cases prestained for ERα were scanned and scored manually using ImageScope viewing software. Corresponding images were then uploaded and assessed according to the web-based ImmunoRatio programme. There was excellent correlation between manual and ImmunoRatio ERα scores (Spearman correlation=0.872; p≥0.001). The manual and ImmunoRatio ERα scores showed only a moderate agreement (κ=0.421; Weighted kappa=0.874 (CI 0.839 to 0.902)), most probably due to lack of specificity of the algorithm to differentiate between cancer and non-cancer nuclei. Further development to enable differentiation of cancer and non-cancer elements should improve the specificity of the application. Our results support the use of ImmunoRatio software for analysing ERα immunohistochemistry in breast cancer tissues for the purposes of research.

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DO - 10.1136/jclinpath-2013-201680

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JF - Journal of Clinical Pathology

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External validation of the ImmunoRatio image analysis application for ERα determination in breast cancer

Abstract

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