Abstract PO3-07-03: Development & Validation of an AI-supported Workflow for Clinical Scoring of HER2, ER, PR & Ki67 Immunohistochemistry in Breast Cancer Tissue

Abstract Background: Immunohistochemical evaluation of HER2 status, the hormone receptors ER and PR, and the proliferation marker Ki67 forms part of the routine clinical diagnostic pathway for invasive breast carcinomas, and is the cornerstone of treatment stratification, informing both prognosis and patient management. Pathologist scoring of immunohistochemistry (IHC) at the microscope is time-consuming and prone to significant inter- and intra-observer variability. We developed HALO Breast AI, a decision-support system designed to improve efficiency and diagnostic accuracy through automating whole slide image (WSI) scoring. Here, we present preliminary results of a validation study of HALO Breast AI. Methods: HALO Breast AI was developed with routine diagnostic cases sourced from three institutes. The algorithm was trained using 107,755 pathologist-reviewed annotations to identify and threshold DAB-positive tumor cells within automatically segmented tumor regions. Internal validation was conducted on 80 unseen WSI, using 60,012 pathologist-reviewed annotations to assess analytical performance. Comparison of the algorithm scores to the mode of 3 expert pathologists (where at least 2 out of 3 agreed) was used to assess consensus agreement. Clinical performance and generalizability were assessed by comparing the algorithm scores to clinical data from two independent external institutes across 200 unseen WSI (n=50 per marker) from institute one and 300 unseen WSI (n=100 per marker [ ER, PR 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-03.