AI microscope facilitates accurate interpretation of HER2 immunohistochemical scores 0 and 1+ in invasive breast cancer.

Accurate interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) scores 0 and 1+ is crucial for treating HER2-low breast cancer patients with antibody-drug conjugates. To improve diagnostic precision, we developed models using 698 retrospectively collected HER2 IHC slides of breast cancer and tested them on an additional 501 slides reviewed by one junior and three senior pathologists. The artificial intelligence (AI)-based models included an invasive breast cancer (IBC) region segmentation model (Model I) and a nuclei detection model (Model II). Model I achieved mean intersection over union (MIoU) scores of 0.879 and 0.880 at 20× and 40× magnifications, and Model II's F1-scores were 0.866 and 0.878. The proposed AI microscope based on Models I and II achieved F1 scores of 0.878 and 0.906 and accuracies of 0.856 and 0.890 for interpreting IHC scores of 0 and 1+ at 20× and 40×, respectively, which was superior to that of a junior pathologist with an F1 score of 0.871 and an accuracy of 0.848. Additionally, the AI microscope showed high consistency with the interpretation results from the senior pathologists, reaching kappa values of 0.703 at 20× and 0.774 at 40×. This AI microscope has the potential to enhance the interpretation accuracy of HER2 IHC score in clinical settings.