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

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...

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Bibliographic Details
Published in:Journal of clinical pathology Vol. 74; no. 7; pp. 469 - 474
Main Authors: Gentles, Lucy, Howarth, Rachel, Lee, Won Ji, Sharma-Saha, Sweta, Ralte, Angela, Curtin, Nicola, Drew, Yvette, O'Donnell, Rachel Louise
Format: Journal Article
Language:English
Published: London BMJ Publishing Group LTD 01.07.2021
Subjects:
Automation
Biomarkers
Kinases
Ovarian cancer
United Kingdom > UK
ISSN:0021-9746, 1472-4146, 1472-4146
Online Access:Get full text
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Summary: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.
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ISSN:0021-9746
1472-4146
1472-4146
DOI:10.1136/jclinpath-2020-207081