Abstract PD11-10: Optimizing HER2 Diagnostic Pathways: AI Assistance Enriches Gene Amplified Cases in Equivocal Category and Reduces Turnaround Time

Abstract Background HER2 expression is a key prognostic and treatment-influencing factor in breast cancer and is assessed for all invasive breast carcinoma (BC) cases. As with all immunohistochemistry (IHC) staining, the visual interpretation of HER2 expression is subjective and semi-quantitative, which leads to intra- and inter-pathologist variability. One of the accepted reference standards for determining HER2 status in equivocal HER2 2+ cases is in situ hybridization (ISH) to assess gene amplification from FFPE tumor samples. However, assessment of gene amplification by ISH can significantly increase result turnaround times. Here, we evaluate the clinical utility (accuracy and user feedback) of an artificial intelligence (AI)-aided HER2 IHC scoring solution on whole-slide images of HER2 IHCs of breast samples. Methods The study cohort included both biopsies and excisions from 2,300 patients from 13 US, EU, and UK clinical laboratories, including academic medical centers and reference/private laboratories. HER2 IHC slides of diverse BC subtypes from primary and metastatic tumors were stained with anti-HER2 antibody (4B5, VENTANA) and scanned with different scanners. This observational two-arm multi-reader study compared the performance of 28 pathologists (“readers”) on HER2 scoring (each pathologist reviewed 50-200 slides) unassisted vs. aided by an AI HER2 solution (Ibex Breast HER2®). The AI tool automatically detects the invasive tumor area and on slide controls, classifies tumor cells based on their HER2 staining pattern, and derives a slide-level HER2 IHC score by applying ASCO/CAP guidelines. Both study arms were compared to the ground truth (GT), established as majority score of three expert breast pathologists who reviewed the slides manually and included ISH results for HER2 IHC 2+ slides. Results Across 8,226 slide reviews, pathologists achieved a significant improvement in overall accuracy when assisted by the AI tool, increasing from 76.4% without AI assistance to 82.0% (P=0.0000 by McNemar test). Importantly, accuracy improved across all clinical HER2 categories (null, ultralow, low, positive), increasing from 79.3% to 84.4% with AI support. Interestingly, AI-assisted pathologists correctly classified fewer cases as equivocal (HER2 2+), reducing the proportion of slides requiring ISH testing from 29.0% to 18.6%, which may translate to a 35% reduction in patient turnaround time. Feedback from reader pathologists' user survey revealed that AI-assistance leads to increased confidence in HER2 scoring accuracy and consistency. Additionally, 83% of pathologists expressed motivation to continue using AI-assisted HER2 scoring over their standard manual scoring. Conclusions AI-assisted HER2 scoring significantly improved both diagnostic accuracy and score consistency across diverse breast cancer cases and clinical settings. Importantly, the use of AI reduced equivocal (HER2 2+) classifications, enriched HER2 gene amplification status in cases classified as HER2 2+, potentially enabling faster diagnostic turnaround. Pathologist feedback indicated increased confidence in scoring and strong motivation to adopt AI-assisted workflows over manual interpretation. These findings highlight the value of AI systems in biomarker interpretation, providing pathologists with enhanced decision-making tools with explainability at the individual cell level and improving diagnostic precision in HER2 IHC interpretation. Improved scoring accuracy and consistency can support more reliable patient stratification and treatment selection, helping to ensure that patients receive the most appropriate HER2-targeted therapies. Citation Format: M. Vecsler, S. Krishnamurthy, S. Schnitt, A. Vincent-Salomon, E. Provenzano, R. Canas-Marques, L. Arnould, E. Shearon, P. Chandra, P. Borkowski, S. Declercq, J. Loane, A. Gunavardhan, L. Di Tommaso, V. Krauss, P. Richard, M. Brevet, M. Grinwald, D. Mevorach, R. Ziv, S. Stein, G. Mallel, M. J. T. Senior, R. J. Hill, J. Longshore, S. Judith, C. Linhart. Optimizing HER2 Diagnostic Pathways: AI Assistance Enriches Gene Amplified Cases in Equivocal Category and Reduces Turnaround Time abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD11-10.