Multimodal deep learning to predict prognosis in adult and pediatric brain tumors.

ABSTRACT:

Background

The introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer, different data modalities may reveal different disease characteristics, and the integration of imaging with genomic data has the potential to unravel additional information than when using these data sources in isolation. Here, we propose a DL framework that combines these two modalities with the aim to predict brain tumor prognosis.

Methods

Using two separate glioma cohorts of 783 adults and 305 pediatric patients we developed a DL framework that can fuse histopathology images with gene expression profiles. Three strategies for data fusion were implemented and compared: early, late, and joint fusion. Addit

SUBMITTER: Steyaert S 

PROVIDER: S-EPMC10060397 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature