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

• Published in: JCO precision oncology Vol. 8; p. e2400353
• Main Authors: Krishnamurthy, Savitri, Schnitt, Stuart J, Vincent-Salomon, Anne, Canas-Marques, Rita, Colon, Eugenia, Kantekure, Kanchan, Maklakovski, Marina, Finck, Wilfrid, Thomassin, Jeanne, Globerson, Yuval, Bien, Lilach, Mallel, Giuseppe, Grinwald, Maya, Linhart, Chaim, Sandbank, Judith, Vecsler, Manuela
• Format: Journal Article
• Language: English
• Published: United States 01-10-2024
• Subjects: Artificial Intelligence; Breast Neoplasms - pathology; Female; Humans; Immunohistochemistry - methods; Observer Variation; Receptor, ErbB-2 - analysis
• ISSN: 2473-4284
• DOI: 10.1200/PO.24.00353

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.
A two-arm multireader study of 120 HER2 IHC whole-slide images from four sites assessed HER2 scoring by four surgical pathologists without and with the aid of an AI HER2 solution. Both arms were compared with high-confidence ground truth (GT) established by agreement of at least four of five breast pathology subspecialists according to ASCO/College of American Pathologists (CAP) 2018/2023 guidelines.
The mean interobserver agreement among GT pathologists across all HER2 scores was 72.4% (N = 120). The AI solution demonstrated high accuracy for HER2 scoring, with 92.1% agreement on slides with high confidence GT (n = 92). The use of the AI tool led to improved performance by readers, interobserver agreement increased from 75.0% for digital manual read to 83.7% for AI-assisted review, and scoring accuracy improved from 85.3% to 88.0%. For the distinction of HER2 0 from 1+ cases (n = 58), pathologists supported by AI showed significantly higher interobserver agreement (69.8% without AI
87.4% with AI) and accuracy (81.9% without AI
88.8% with AI).
This study demonstrated utility of a fully automated AI solution to aid in scoring HER2 IHC accurately according to ASCO/CAP 2018/2023 guidelines. Pathologists supported by AI showed improvements in HER2 IHC scoring consistency and accuracy, especially for distinguishing HER2 0 from 1+ cases. This AI solution could be used by pathologists as a decision support tool for enhancing reproducibility and consistency of HER2 scoring and particularly for identifying HER2-low breast cancers.