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Integration of computer-aided automated analysis algorithms in the development and validation of immunohistochemistry biomarkers in ovarian cancer

Lookup NU author(s): Lucy Gentles, Professor Nicola CurtinORCiD, Dr Yvette DrewORCiD, Dr Rachel O'DonnellORCiD


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© Author(s) (or their employer(s)) 2020. In an era when immunohistochemistry (IHC) is increasingly depended on for histological subtyping, and IHC-determined biomarker informing rapid treatment choices is on the horizon; reproducible, quantifiable techniques are required. This study aimed to compare automated IHC scoring to quantify 6 DNA damage response protein markers using a tissue microarray of 66 ovarian cancer samples. Accuracy of quantification was compared between manual H-score and computer-aided quantification using Aperio ImageScope with and without a tissue classification algorithm. High levels of interobserver variation was seen with manual scoring. With automated methods, inclusion of the tissue classifier mask resulted in greater accuracy within carcinomatous areas and an overall increase in H-score of a median of 11.5% (0%-18%). Without the classifier, the score was underestimated by a median of 10.5 (5.2- 25.6). Automated methods are reliable and superior to manual scoring. Fixed algorithms offer the reproducibility needed for high-throughout clinical applications.

Publication metadata

Author(s): Gentles L, Howarth R, Lee WJ, Sharma-Saha S, Ralte A, Curtin N, Drew Y, O'Donnell RL

Publication type: Article

Publication status: Published

Journal: Journal of Clinical Pathology

Year: 2021

Volume: 74

Issue: 7

Pages: 469-474

Print publication date: 01/07/2021

Online publication date: 19/11/2020

Acceptance date: 07/09/2020

ISSN (print): 0021-9746

ISSN (electronic): 1472-4146

Publisher: BMJ Publishing Group


DOI: 10.1136/jclinpath-2020-207081

PubMed id: 33214200


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