A recent groundbreaking effort compiled multiple image-based features extracted from multiple sources, i.e., H&E-stained WSIs and IHCs (PD-L1, CD8, CD163), quantitative and qualitative breast cancer biomarker results (ER, PR, HER2), and patient demographic and clinical features, e.g., age, to develop a predictive ML model for NAC response in TNBCs and HER2+ breast cancers. The gene discussed is PGR; the disease is breast cancer.