Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

ABSTRACT: Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated four distinct state-of-the-art deep learning architectures, which include ResNet101, Inception-v3, EfficientNet (with six different var

SUBMITTER: Rahaman MM 

PROVIDER: S-EPMC10442349 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature