A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma.

Frederik WesselsMax SchmittEva Krieghoff-HenningMalin NientiedtFrank WaldbilligManuel NeubergerMaximilian C KriegmairKarl-Friedrich KowalewskiThomas S WorstMatthias SteegZoran V PopovicTimo GaiserChristof von KalleJochen S UtikalStefan FröhlingMaurice S MichelPhilipp NuhnTitus Josef Brinker

Published in: World journal of urology (2023)

The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.

Keyphrases

cancer therapy
renal cell carcinoma
end stage renal disease
newly diagnosed
ejection fraction
machine learning
peritoneal dialysis
deep learning
prognostic factors
drug delivery
convolutional neural network
patient reported outcomes
current status