Abstract
BACKGROUND: Neoadjuvant therapy (NAT) is a standard breast cancer treatment, but patient response varies significantly. Predictive markers can guide treatment decisions, yet their interpretation suffers from inter-pathologist variability due to breast cancer's complex histology and heterogeneity. Artificial intelligence (AI) applied to image-based omics offers potential to enhance pathological interpretation precision and consistency. METHODS: This review synthesizes existing literature on the application of AI in breast cancer pathology. We specifically focused on identifying and summarizing research that utilizes diverse histopathological features-including morphological characteristics, molecular markers, gene expression profiles, and multidimensional omics data-to predict NAT response in breast cancer patients. RESULTS: AI demonstrates significant capabilities in automatically recognizing histopathological patterns and predicting NAT efficacy. It shows promise as a tool for patient stratification in precision oncology. Research utilizing various pathological feature types (morphological, molecular, genomic, multi-omics) for NAT response prediction is actively evolving. While AI models integrating multi-omics features show potential, challenges remain in robustly predicting NAT outcomes. CONCLUSION: AI-based pathology represents a prospective and powerful decision-support tool for predicting breast cancer NAT response. Despite existing challenges, particularly with complex multi-omics models, AI holds great potential to assist clinical oncologists in optimizing future cancer treatment management.