Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

pathological complete response (pcr) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (nac). predictive biomarkers of treatment response are crucial for guiding treatment decisions. with the hypothesis that histological information on tumor biopsy images could predict nac response in breast cancer, we proposed a novel deep learning (dl)-based biomarker that predicts pcr from images of hematoxylin and eosin (he a prediction model of pcr constructed by integrating stils, subtype and pcr-score yielded a mean auc of 0.890, outperforming the baseline stil-subtype model by 0.051 (0.839, p  =  0.001). the dl-based pcr-score from histological images is predictive of pcr better than stils and subtype, and holds the great potentials for a more accurate stratification of patients for nac.