Abstract 5437: Vision transformers for breast cancer human epidermal growth factor receptor 2 (HER2) expression staging without immunohistochemical (IHC) staining
Gelan Ayana, Eonjin Lee, Se‐woon Choe · Cancer Research · 2023
Abstract Purpose: Staging of human epidermal growth factor receptor 2 (HER2) expression is crucial for evaluating the effectiveness of breast cancer treatment. However, it involves an expensive and challenging immunohistochemical (IHC) staining in addition to hematoxylin and eosin (H&E) staining. Here, we argue that customized vision transformers are effective in breast cancer HER2 expression staging using only H&E-stained images. Methods: Our algorithm has three modules: a localization module to weakly localize the necessary features of an image via spatial transformers, an attention module to learn global features from the image using vision transformers, and a loss module that determines the closest one to a HER2 expression level from input images by calculating the ordinal loss. We utilized a publicly available breast cancer HER2 expression dataset, the breast cancer immunohistochemical image (BCI) dataset, which contains paired H&E and IHC stained images. Results were calculated with 95% confidence intervals and t-test was used to evaluate the significance of the proposed model against other models. Results: Our approach achieved area under receiver operating characteristics curve (AUC), precision, sensitivity, and specificity of 0.9202±0.01, 0.922±0.01, 0.876±0.01, and 0.959±0.02, respectively, averaged over five-fold cross-validation, in staging HER2 expression. The proposed method showed better performance than the conventional vision transformer model and state-of-the-art convolutional neural network models with statistical significance of p<0.01 in all cases. Conclusion: Our findings are important in assisting HER2 expression staging in breast cancer treatment, while avoiding the need for an expensive and time-consuming IHC staining procedure. Comparison of the proposed model with state-of-the-art models. CNN-convolutional neural network Study Neural network Model AUC Couture et al. (2018) CNN VGG16 0.75 Shamai et al. (2019) CNN ResNet50 0.74 Yang et al. (2021) CNN ResNet50 0.76 Naik et al. (2020) CNN ResNet50 0.78 Khater et al. (2021) CNN ShuffleNet 0.75 Conde-Sousa et al. (2022) CNN EfficientNet 0.88 Farahmand et al. (2022) CNN InceptionV3 0.90 Proposed Vision transformer vitb_16 0.92 Citation Format: Gelan Ayana, Eonjin Lee, Se-woon Choe. Vision transformers for breast cancer human epidermal growth factor receptor 2 (HER2) expression staging without immunohistochemical (IHC) staining. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5437.