Integration of computer-aided automated analysis algorithms in the development and validation of immunohistochemistry biomarkers in ovarian cancer. Issue 7 (19th November 2020)

• Record Type: Journal Article
• Authors: Gentles, Lucy; Howarth, Rachel; Lee, Won Ji; Sharma-Saha, Sweta; Ralte, Angela; Curtin, Nicola; Drew, Yvette; O'Donnell, Rachel Louise
• Is Part Of: Journal of clinical pathology. Volume 74:Issue 7(2021)
• Page Start: 469
• Page End: 474
• Publication Date: 2020-11-19
• Subjects: ovary -- carcinoma -- antibodies -- cell biology -- immunohistochemistry; Pathology -- Periodicals; Pathology, Molecular -- Periodicals; 616.0705
• DOI: 10.1136/jclinpath-2020-207081
• Languages: English
• ISSNs: 0021-9746

Abstract

Abstract : 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.