Artificial Intelligence‐Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer - Juan Ji; Fanglei Duan; Qiong Liao; Hao Wang; Shiwei Liu; Yang Liu; Zongyao Huang (Cancer Medicine)
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.