Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks.

ABSTRACT:

Motivation

Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression.

Results

We conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to p

SUBMITTER: Weitz P 

PROVIDER: S-EPMC9237721 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature