Abstract 5436: Developing artificial intelligence algorithms to predict response to neoadjuvant chemotherapy in HER2-positive breast cancer

Abstract Increasing implementation of whole slide image (WSI) and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, such as quantification of biomarkers, aids in diagnosis and detection of lymph node metastasis. However, predicting therapy response in cancer patients from pre-treatment histopathologic images remains a challenging task, limited by poor understanding of tumor immune microenvironment. In this study, we aimed to develop AI models using multi-source histopathologic images to predict neoadjuvant chemotherapy (NAC) response in HER2-positive (HER2+) breast cancers. First, pretreatment tumor tissues were stained with Hematoxylin and Eosin (H&E) and multiplex immunohistochemistry (IHC) including tumor immune microenvironment markers (PD-L1: immune checkpoint protein; CD8: marker for cytotoxic T-cells; and CD163: marker for type 2 macrophages). Next, we developed an AI-based pipeline to automatically extract histopathologic features from H&E and multiplex IHC WSIs. The pipeline included: A) H&E tissue segmentation based on DeepLabV3 model to generate stroma, tumor, and lymphocyte-rich regions. B) IHC marker segmentation to segment CD8, CD163, and PD-L1 stained cells. C) H&E and IHC non-rigid registration to match H&E and IHC images since they were stained from different levels of tissue. D) Image-based registration and segmentation histopathologic feature construction. A total of 36 histopathological features were constructed to represent tumor immune microenvironment characteristics such as ratios of PD-L1, CD8 and CD163 in tumoral, stromal or lymphocyte-rich regions. They were used to train machine learning (ML) models to predict NAC response in a training dataset with 62 HER2+ breast cancers (38 with complete and 24 with incomplete response). The ML model using logistic regression demonstrated the best performance with an area under curve (AUC) of 0.8975. We also tested ML models using pathologists-derived histopathologic features, but the best performed model showed an AUC of 0.7880. Finally, the developed logistic regression ML model was tested on an external validation dataset with 20 HER2+ breast cancers (10 with complete and 10 with incomplete response) and yielded an AUC of 0.9005. In summary, we described an automatic, accurate and interpretable AI-based pipeline to extract histopathologic features from H&E and IHC WSIs and then used these features to develop machine learning model to accurately predict NAC response in HER2+ breast cancers. The ML model using AI-based extracted features outperformed the model using features manually generated by pathologists. Citation Format: Zhi Huang, Anil V. Parwani, Kun Huang, Zaibo Li. Developing artificial intelligence algorithms to predict response to neoadjuvant chemotherapy in HER2-positive breast cancer. [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 5436.