Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

ABSTRACT:

Background

Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.

Results

We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin

SUBMITTER: Roetzer-Pejrimovsky T 

PROVIDER: S-EPMC11345537 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature