Guestimating Molecular Subtyping of Breast Cancer by Ki67 in the Era of Artificial Intelligence.

Aims: This study aimed to compare the performance of immunohistochemistry (IHC)-based luminal subtyping of breast cancer against gene expression panels at our institute and to evaluate a CE-certified artificial intelligence (AI) Ki67 image analysis program for improving subtyping accuracy. Methods and Results: We retrospectively analysed IHC-based luminal subtyping in breast cancer biopsies diagnosed at our institute from 2019 to 2022 (n = 1736), and identified n = 104 (Oncotype DX) and n = 64 (EndoPredict) cases with gene expression tests requested by clinicians. Of the eligible ER-positive HER2-negative cases, 11.9% (n = 168) underwent multigene testing. After excluding incomplete data (n = 22), gene tests revealed 48 patients (32.9%) would benefit from chemotherapy, 86 (58.9%) could avoid it and 12 (8.2%) had inconclusive results. A moderate correlation was observed between Ki67 and EndoPredict EPClin scores (r = 0.47-0.58) and a weak correlation between Ki67 and Oncotype DX recurrence scores (r = 0.31-0.38). Ki67 scores were significantly higher in luminal B compared with luminal A tumours (difference of 9.1-15.2, p < 0.01). No significant difference was found between mean Ki67 scores reported by pathologists and AI (pathologists' mean Ki67 17.36 vs. AI mean Ki67 18.36, n = 146, p = 0.456) and the accuracy of luminal subtyping was similar between pathologists and AI (accuracy pathologists 66.4% vs. AI 62.7%, p = 0.538). Conclusions: Our data provides a snapshot of the real-world allocation of multigene testing in early breast cancer, and supports other studies in highlighting the discrepancy between IHC-based and gene-based luminal subtyping. Ki67 evaluation remained consistent over time, and the use of AI for Ki67 scoring did not enhance the accuracy of IHC-based luminal subtyping.