Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology

arXiv:2404.16397v1 Announce Type: cross
Abstract: The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We …

abstract arxiv cancer cells cs.cv cs.lg deep learning eess.iv however interactions patient prediction q-bio.qm type