Immunotherapy for programmed death 1 (PD-1) and programmed death ligand-1 (PD-L1) is one of the promising recently developed treatments for several types of cancer. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.īreast cancer became the leading cause of death in women ages 20 to 59 and the most diagnosed cancer as of 2021, accounting for 12% of all new annual cancer cases worldwide 1. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 – 0.93. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. ![]() The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. ![]() ![]() Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies.
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