Precision oncology: AI's breakthrough in TNBC chemoresistance prediction

Triple-negative breast cancer (TNBC) is an aggressive subtype with limited targeted therapies and variable response to neoadjuvant chemotherapy (NAC), with only 30-40% of patients achieving optimal outcomes. Predicting NAC resistance at diagnosis is a critical unmet clinical need to avoid unnecessary toxicity and delays in alternative treatments. Artificial intelligence (AI) applied to digitized histopathology offers a promising solution. Recent advances include NACNet, a spatial graph convolutional network incorporating tumor microenvironment context with high predictive accuracy; morphometric analysis using convolutional neural networks to identify histological signatures of resistance; and ensemble deep learning frameworks guided by expert cognition to enhance generalizability across multi-center datasets. While current models face challenges in external validation and require broader datasets to minimize bias, these developments demonstrate AI's growing role in individualized treatment planning for TNBC. By integrating biological insight with advanced computational modeling, AI holds potential to revolutionize chemoresistance prediction and precision oncology.