Work

A Robust Automated Measure of Average Antibody Staining in Immunohistochemistry Images

Year: 2009

Type: article

Abstract: Identifying and scoring cancer markers plays a key role in oncology, helping to characterize the tumor and predict the clinical course of the disease. The current method for scoring immunohistochemistry (IHC) slides is labor intensive and has inherent issues of quantitation. Although multiple attempts have been made to automate IHC scoring in the past decade, a major limitation in these efforts has been the setting of the threshold for positive staining. In this report, we propose the use of an averaged threshold measure (ATM) score that allows for automatic threshold setting. The ATM is a single multiplicative measure that includes both the proportion and intensity scores. It can be readily automated to allow for large-scale processing, and it is applicable in situations in which individual cells are hard to distinguish. The ATM scoring method was validated by applying it to simulated images, to a sequence of images from the same tumor, and to tumors from different patient biopsies that showed a broad range of staining patterns. Comparison between the ATM score and manual scoring by an expert pathologist showed that both methods resulted in essentially identical scores when applied to these patient biopsies. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials.

Source: Journal of Histochemistry & Cytochemistry

DOI: 10.1369/jhc.2009.953554

Authors: Kingshuk Roy Choudhury, Kevin J. Yagle, Paul E. Swanson, Kenneth A. Krohn, Joseph G. Rajendran

Institutions: University College Cork, University of Washington

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Cancer Institute