Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer.

ABSTRACT:

Background

It has been believed that traditional handcrafted radiomic features extracted from magnetic resonance imaging (MRI) of tumors are normally shallow and low-ordered. Recent advancement in deep learning technology shows that the high-order deep radiomic features extracted automatically from tumor images can capture tumor heterogeneity in a more efficient way. We hypothesize that MRI-based deep radiomic phenotypes have significant associations with molecular profiles of breast cancer tumors. We aim to identify deep radiomic features (DRFs) from MRI, evaluate their significance in predicting breast cancer (BC) clinical characteristics and explore their associations with multi-level genomic factors.

Methods

A denoising autoencoder was built to retrospectively extract 4,

SUBMITTER: Liu Q 

PROVIDER: S-EPMC9872423 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature