Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges

Marie Brevet; Zaibo Li; Anil Parwani

doi:10.1016/j.jpi.2023.100343

Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges

J Pathol Inform. 2023 Nov 4:15:100343. doi: 10.1016/j.jpi.2023.100343. eCollection 2024 Dec.

Authors

Marie Brevet¹, Zaibo Li², Anil Parwani²

Affiliations

¹ Cypath-RB, Lyon, France.
² Department of Pathology, The Ohio State University, Columbus, OH, USA.

Abstract

For the past 2 decades, pathologists have been accustomed to reporting the HER2 status of breast cancer as either positive or negative, based on HER2 IHC. Today, however, there is a clinical imperative to employ a 3-tier approach to interpreting HER2 IHC that can also identify tumours categorised as HER2-low. Meeting this need for a finer degree of discrimination may be challenging, and in this article, we consider the potential for the integration of computational approaches to support pathologists in achieving accurate and reproducible HER2 IHC scoring as well as outlining some of the practicalities involved.

Keywords: HER2; HER2-low; breast carcinoma; computational pathology; digital pathology; immunohistochemistry.

Publication types

Review