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

We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.