Deep learning in cancer pathology: a new generation of clinical biomarkers.

ABSTRACT: Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histolog

SUBMITTER: Echle A 

PROVIDER: S-EPMC7884739 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature