Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.

ABSTRACT: Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of ke

SUBMITTER: Lee SH 

PROVIDER: S-EPMC9597228 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature