Fully Automated Artificial Intelligence Solution for Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Scoring in Breast Cancer: A Multireader Study.

Savitri Krishnamurthy¹, Stuart J Schnitt^2,3, Anne Vincent-Salomon⁴, Rita Canas-Marques⁵, Eugenia Colon⁶, Kanchan Kantekure⁷, Marina Maklakovski⁸, Wilfrid Finck⁹, Jeanne Thomassin⁹, Yuval Globerson¹⁰, Lilach Bien¹⁰, Giuseppe Mallel¹⁰, Maya Grinwald¹⁰, Chaim Linhart¹⁰, Judith Sandbank^10,11, Manuela Vecsler¹⁰

¹Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.

²Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.

³Breast Oncology Program, Dana-Farber Cancer Institute, Boston, MA.

⁴Department of Pathology, Institut Curie, PSL University, Paris, France.

⁵Department of Pathology, Champalimaud Foundation, Lisbon, Portugal.

⁶Department of Pathology, Unilabs, St Görans Hospital, Stockholm, Sweden.

⁷Beth Israel Deaconess, Harvard Medical School, Boston, MA.

⁸Department of Pathology, Assuta Ashdod Medical Center, Ashdod, Israel.

⁹MediPath, Frejus, France.

¹⁰Ibex Medical Analytics, Tel Aviv, Israel.

¹¹Institute of Pathology, Maccabi Healthcare Services, Rehovot, Israel.

PubMed PMC DOI PDF

Abstract

PURPOSE: The proven efficacy of human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate therapy for treating HER2-low breast cancers necessitates more accurate and reproducible HER2 immunohistochemistry (IHC) scoring. We aimed to validate performance and utility of a fully automated artificial intelligence (AI) solution for interpreting HER2 IHC in breast carcinoma. MATERIALS AND…

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