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Aims: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)‐empowered microscope to improve Ki67 scoring concordance. Methods and results: We developed an AI‐empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67‐stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI‐empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI‐empowered microscope. Conclusion: The AI‐empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment. [ABSTRACT FROM AUTHOR]

The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserver variability in the evaluation of HER2 staining by visual examination. In this study, we aim to propose an artificial intelligence (AI)-assisted microscope to improve the HER2 assessment accuracy and reliability. Our AI-assisted microscope was equipped with a conventional microscope with a cell-level classification-based HER2 scoring algorithm and an augmented reality module to enable pathologists to obtain AI results in real time. We organized a three-round ring study of 50 infiltrating duct carcinoma not otherwise specified (NOS) cases without neoadjuvant treatment, and recruited 33 pathologists from 6 hospitals. In the first ring study (RS1), the pathologists read 50 HER2 whole-slide images (WSIs) through an online system. After a 2-week washout period, they read the HER2 slides using a conventional microscope in RS2. After another 2-week washout period, the pathologists used our AI microscope for assisted interpretation in RS3. The consistency and accuracy of HER2 assessment by the AI-assisted microscope were significantly improved (p < 0.001) over those obtained using a conventional microscope and online WSI. Specifically, our AI-assisted microscope improved the precision of immunohistochemistry (IHC) 3 + and 2 + scoring while ensuring the recall of fluorescent in situ hybridization (FISH)-positive results in IHC 2 + . Also, the average acceptance rate of AI for all pathologists was 0.90, demonstrating that the pathologists agreed with most AI scoring results. [ABSTRACT FROM AUTHOR]

The expression status of HER2 is an important factor in evaluating the prognosis of breast cancer. We found that the positivity rate of HER2 varied with the diameter of invasive lesions in early breast cancer.We aimed to explore the relationship between the change of HER2 positivity rate of early breast cancer and the diameter of the invasive foci and its clinical significance. A total of 217 patients with microinvasive breast cancer and T1a stage breast cancer were enrolled in this study. Machine learning algorithm was used to extract morphological features of invasive lesions in early breast cancer. Using Spearman to analysis the clinicopathological and morphological features related to HER2 expression McNemar test was used to analyze the consistency of HER2 expression between the carcinoma in situ area and the invasive cancer area. In early breast cancer, the diameter of the invasive foci was strongly negatively correlated with the expression status of HER2 (rho=−0.468, p < 0.001). As the diameter of the invasive foci increases, the HER2 positivity rate gradually decreases. When the diameter of the invasive foci> 2 mm, the positivity rate of HER2 was significantly reduced (from 52.6% to 16.1%, p < 0.001), which was close to the positivity rate of HER2 in ordinary invasive breast cancer. Moreover, most of the clinicopathological characteristics of breast cancer with an invasive lesion diameter of 1–2 mm and DCIS-MI were not significantly different (p > 0.05). Among 217 patients, the consistency rate of HER2 expression in carcinoma in situ and invasive foci areas was 97.7% (212/217), and there was no significant difference in HER2 expression status (p = 0.25 and p = 0.50, respectively). We recommend that breast cancer with an invasive lesion diameter of 1–2 mm should be classified as microinvasive breast cancer. In early breast cancer,the expression status of HER2 in the invasive foci area can refer to the HER2 expression status in carcinoma in situ area. However, it needs further support from a large amount of data and follow-up results. [ABSTRACT FROM AUTHOR]