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

PURPOSE: To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). METHODS: Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision t...

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Detalles Bibliográficos
Autores principales: Wessels, Frederik, Schmitt, Max, Krieghoff-Henning, Eva, Nientiedt, Malin, Waldbillig, Frank, Neuberger, Manuel, Kriegmair, Maximilian C., Kowalewski, Karl-Friedrich, Worst, Thomas S., Steeg, Matthias, Popovic, Zoran V., Gaiser, Timo, von Kalle, Christof, Utikal, Jochen S., Fröhling, Stefan, Michel, Maurice S., Nuhn, Philipp, Brinker, Titus J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415487/
https://www.ncbi.nlm.nih.gov/pubmed/37382622
http://dx.doi.org/10.1007/s00345-023-04489-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415487/
https://www.ncbi.nlm.nih.gov/pubmed/37382622
http://dx.doi.org/10.1007/s00345-023-04489-7

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