Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study
Xiangyang Zhang, Yang Chen, Changjing Cai, Yifeng Wang, Jun Tan, Zijie Fang, Wei Le, Zhuchen Shao, Liwen Wang, Tiezheng Qi, Yihan Liu, Zhaohui Jiang, Yin Li, Ying Han, Tibera K. Rugambwa, Shan Zeng, Haoqian Wang, Hong Shen, Yongbing Zhang · International Journal of Surgery · 2025
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.